>> Narrator: Live from San Juan, Puerto Rico It's theCUBE, covering Blockchain Unbound Brought to you by Blockchain Industries (upbeat music) >> Hey, welcome back everybody, we're live in Puerto Rico for the cryptocurrency, global blockchain, decentralized internet, Cube coverage in Puerto Rico part of Blockchain Unbound. I'm John Furrier, host of theCUBE here, also co-founder of SiliconANGLE Media Inc. And, we're here with a first Cube ever, father/son Cube segment where we're going to kind of break down a summary of the show but mainly get the take from a 22 year old. Here with me is my son Alex Furrier who's been doing the schedule and greeting all the guests. Alec has been also demoing our platform that we haven't formally announced but also Not that we have to but it's out there. theCUBE platform, all the back-end data Because it really is getting everyone here excited So, Alec, welcome to theCUBE. >> Thanks, great to be on, finally, after all these years (John chuckles) to be on, it's an honor. >> Well, thanks for all the hard work you did on the schedule but you're a young gun, you're 22 years old. This is an exciting crypto world for your generation. What's your reaction to the commentary you've heard, the stories you've heard, what's the young perspective on cryptocurrency, blockchain, what's the view? >> Totally, it's a totally crazy culture, right? So, there's a very big influx of young talent and talented minds at that, right? And, this is really changing the revolution landscape. It's accelerating the tech. These ideas are being freely shared whereas before there was bottlenecks in the collaboration aspect of the technological field, right? >> You're a gamer, I know that so you're the young eco-system You don't care about data lakes and data centers and cloud computing. What is your generation look at this as an opportunity? What's exciting about it? What's the perspective? >> Well, there's multiple perspectives. The main two I say, there's multiple perspectives. Main two, is one, there's a shit ton of way to make money. And you know, is there a scam? Is there a risk for my business? You know, blockchain is involved. And there's a little bit of that mumbo jumbo going along. But then, there's also the other side that are really into it and really applying the tech and know that this is the best way to collaborate with peers >> What's the coolest thing you've seen? >> The coolest thing I've seen is probably Hashgraph which is actually not on the blockchain and competitors of the blockchain. And that's actually increasing speeds and pretty much making the tech, the back-end infrastructure better. >> So, you dropped out of UCSB, you're going to maybe go back to school but you're also working as a product manager for our crypto project for SiliconANGLE Media, theCUBE, Cube Network, you were giving demos. What is, what are we doing? How would you explain what we're doing? And, what was some of the reactions to the demo that you were giving? >> All great reactions so far. People are very excited what we're building which is a reputation centrality metric. And, what this does, is allows us to track, what users are talking about, and where they're talking about it. And actually, rank their reputation leaderboard rankings by topic, by frequency, by impact down reverb in the entire network. And that allows us to appropriate connections between two people who have different social, culture and professional topics that they talk about. And allow them to create more value for the entire platform, for the community and more importantly, themselves. >> What is, what does that mean, what problem are we solving? >> So, we're solving the Facebook ad word problem of the old generation which is you as a user do not own your data. Right? >> Yep. >> So now, what we have is this user base struggling to find the monetary value in their social media platforms. But now, we are actually offering a way for them to reverse the paradigm and get paid for interacting with others, creating with others and contributing to the community through all of their social media outlets. >> What was the biggest thing that people reacted to at the demos, the variety of tools we showed them. What was the number one, couple of things that they reacted to, what jumped out at you? >> So, I would say what jumped out is, how blown away these people are. They really are, you know, elevated in their mindset when they think about these concepts. Because it expands their mind and when they realize that I can go and expand someone else's mind and their mind will essentially contribute to the entire community. And everyone's going to grow from one initial idea. >> What are you working on, the project? Please share with the folks, what've you been working on, what specific things that you do and you're managing. What's unique about the technology? Share some color commentary on the project. >> Yeah so right now we have a couple of projects going, and, for now, I'll just talk about the platform side of things which is the more futuristic vision. Specifically, we're creating trending communities so we could actually auto generate stories based on Twitter API data, right? And also, our own platform has even more complex metrics which we'll be rewarding people for, so people will get rewards for using our platform more than the Twitter. But we could still have native content versus in-network content being weighed differently. And so, what we're doing is routing metrics of weighted value with a contextual layer on top through natural language processing and machine learning. >> So, are some people saying "Oh, you're like Steam?" How do you respond to that? >> We're not like Steam. Steam is extremely powerhousey and it's momentum and it doesn't actually do topic weighing Right, so, and we also value attention of the crowd so what we're working on is, what do people influence with their reputation? Whereas Steam, it's like, where do people contribute? How much do they contribute? And so, what we want to do is, we say hey, you know if I get uploads on Reddit that should be weighed in the network somewhere else, right? Instead of having a overall karma, we should have one integrated karmic aspect of a topicality so that if my karma, I'm using karma as an analogy cause Reddit has the up votes karma, down votes karma. >> So what about blockchain, why are we So, how would you explain to someone Okay, you're theCUBE what is the blockchain? What is crypto mean for us? >> So, blockchain, we're using it to add a layer of trust and security to our network. So we want transparency within our network and that means we have to have a ledger for every single engagement, interaction like we tweet on the network, right? >> And the crypto, the token, does what? >> Crypto token will pretty much be able to be cashed out thru Ethereum, right, ERC20 but it would also have a weighted role in our two sided marketplace, bounty ask buy. And, that'll be the main medium of where people identify and exchange their reputation. >> How would you describe out platform to a user out there if they say, what do you like, or what are you disrupting, what aren't you like, what are you guys doing, what you disrupting? And why would I want to use your platform? >> Yeah, so I think we're disrupting, you know, multiple companies, right? And, the one I really associate with is a professional Steamit meets Brave Browser, BAT token versus Steam, right? So, BAT is attention only and attention is valuable. I'm here with you, you have a 20 minute interview with me. That's your attention, that's valuable but it's much more valuable than someone else who isn't interviewing, let's just say, someone who is less fortunate. But, that's also a real time aspect. So there's a time variable, there's a network variable and there's a topicality variable, you know the social graph, you got the interest graph, and then the value graph on top. >> So Alec, so if you had to describe what we do in one sentence, what would it be? Putting you on the spot. >> In one sentence, I would say we would call it, a decentralized media platform with rewards for the user base, based on reputation. >> Alright, my son Alec Furrier is also involved in our crypto project, part of theCUBE network coming soon, house of theCUBE is here, the crypto conference, and what better way to align with the crypto community then demoing our token enabled platform. Congratulations to you, Narendra, Kent, Jeff and the team doing a great job with theCUBE network. Cube alumni are all going to get coins, right? Not yet decided but great work Alec, thanks for sharing. It's theCUBE here, Puerto Rico. I'm John Furrier, my son Alec. Thanks for watching. (upbeat music)

(rousing music) >> Hello everyone. Welcome back to "theCUBE's" day one coverage of Cloud Native Security Con 23. This is going to be an exciting panel. I've got three great guests. I'm Lisa Martin, you know our esteemed analysts, John Furrier, and Dave Vellante well. And we're excited to welcome to "theCUBE" for the first time, Yves Sandfort, the CEO of Comdivision Group, who's coming to us from Germany. As you know, Cloud Native Security Con is a global event. Everyone welcome Yves, great to have you in particular. Welcome to "theCUBE." >> Great to be here. >> Thank you for inviting me. >> Yves, tell us a little bit, before we dig into really wanting to understand your perspectives on the event and get Dave and John's feedback as well, tell us a little bit about you. >> So yeah, talking about me, or talking about Comdivision real quick. We are in the business for over 27 years already. We started as a SaaS company, then became more like an architecture and, and Cloud Native company over the last few years. But what's interesting is, and I think that's, that's, that's really interesting when we look at our industry. It hasn't really, the requirements haven't really changed over the years. It's still security. We still have to figure out how we deal with security. We still have to figure out how we deal with compliance and everything else. And I think therefore, it's more and more important that we take these items more seriously. Also, based on the fact that when we look at it, how development and other things happen nowadays, it's, it's, everybody says it's like open source. It's great because everybody can look into the code. We, I think the last few years have shown us enough example that that's not necessarily solving all the issues, but it's also code and development has changed rapidly when we look at the Cloud Native approach, where it's far more about gluing the pieces together, versus the development pieces. When I was actually doing software development 25 years ago, and had to basically build my code because I didn't have that much internet access for it. So it has evolved, but even back then we had to deal with security and everything. >> Right. The focus on security is, is incredibly important, and the focus keeps growing as you mentioned. This is, guys, and I want to get your perspectives on this. We're going to start with John. This is the first time Cloud Native Security Con is its own event being extracted from, and amplified from KubeCon. John, I want to understand from your perspective, break down the event, what you see, what you've heard, and Cloud Native Security in general. What does this mean to companies? What does it mean to customers? Is this a reality? >> Well, I think that's the topic we want to discuss, and I think Yves background, you see the VMware certification, I love that. Because what VMware did with virtualization, was abstract that from server virtualization, kind of really changed the game on things, and you start to see Cloud Native kind of go that next level of how companies will be operating their business, not just digital transformation, as digital transformation goes to completion, it's total business transformation where IT is everywhere. And so you're starting to see the trends where, "Okay, that's happening." Now you're starting to see, that's Cloud Native Con, or KubeCon, AWS re:Invent, or whatever show, or whatever way you want to look at it. But in, in the past decade, past five years, security has always been front and center as almost a separate thing, and, in and of itself, but the same thing. So you're starting to see the breakout of security conversations around how to make things work. So a lot of operational conversations around what used to be DevOps makes infrastructure as code, and that was great, that fueled that. Then DevSecOps came. So the Cloud Native next level, is more application development at scale, developers driving the standards with developer first thinking, shifting left, I get all that. But down in the lower ends of the stack, you got real operational issues. DNS we've heard in the keynote, we heard about the Colonel, the Lennox Colonel. Things that need to be managed and taken care of at a security level. These are like, seem like in the weeds, but you're starting to see that happen. And the other thing that I think's real about Cloud Native Security Con that's going to be interesting to watch, is Amazon has pretty much canceled all their re:Invent like shows except for two; Re:Invent, which is their annual conference, and Re:Inforce, which is dedicated to securities. So Cloud Native, Linux, the Linux Foundation has now breaking out Cloud Native Con and KubeCon, and now Cloud Native Security Con. They can't call it KubeCon because it's not Kubernetes, but it's like security focus. I think this is the beginning of starting to see this new developer driving, developers driving the standards, and it has it implications, what used to be called IT ops, and that's like the VMwares of the world. You saw all the stuff that was not at developer focus, but more ops, becoming much more in the application. So I think, I think it's real. The question is where does it go? How fast does it develop? So to me, I think it's a real trend, and it's worthy of a breakout, but it's not yet clear of where the landing zone is for people to start doing it, how they get started, what are the best practices. Machine learning's going to be a big part of this. So to me it's totally cool, but I'm not yet seeing the beachhead. So that's kind of my take. >> Dave, our inventor and host of breaking analysis, what's your take? >> So when you, I think when you zoom out, there's some, there's a big macro change that's been going on. I think when you look back, let's say 10, 12 years ago, the, the need for speed far trumped the, the, the security aspect, the governance, the data privacy. It was like, "Yeah, the risks, they're not that great compared to our opportunity." That has completely changed because the risks are now so much higher. And so what's happening, I think there's a, there's a major effort amongst CIOs and CISOs to try to make security not a blocker because it use to be, it still is. "Okay, I got this great initiative." Eh, give it to the SecOps pros, and let them take it for a while before we can go to market. And so a huge challenge now is to simplify, automate, AI comes in, the whole supply chain security, so the, so the companies can not be facing so much friction. And that is non-trivial. I don't think we're anywhere close there, but I think the goal is by, within the next several years, we're going to be in a position, that security, we heard today, is, wasn't designed in to the initial internet protocols. It was bolted on. And so increasingly, the fundamental architecture of the internet, the Cloud, et cetera, is, is seeing designed in security, and, and that is an imperative, or else business is going to come to a grinding halt. >> Right. It's no longer, the bolt no longer works. Yves, what's your perspective on Cloud Native Security, where it stands today? What's in it for customers, whether we're talking about banks, or hospitals, or retailers, what do you think? >> I think when we, when we look at security in the, in the modern world, is we need to as, as Dave mentioned, we need to rethink how we apply it. Very often, security in the past has been always bolted on in the end. If we continue to do that, it'll become more and more difficult, because as companies evolve, and as companies want to bring products and software to market in a much faster and faster way, it's getting more and more difficult if we bolt on the security process at the end. It's like, developers build something and then someone checks security. That's not going to work any longer. Especially if we also consider now the changes in the industry. We had Stack Overflow over the last 10 years. If I would've had Stack Overflow 15, 20, what, 25 years ago when I was a developer, it would've changed a hell lot. Looking at it now, and looking at it what we had in the last few weeks, it's like where nearly all of my team members say is like finally I don't need any script kiddies anymore because I can't go to (indistinct) who writes the code for me. Which is on one end great, because it enables us to solve certain problems in a much higher pace. But the challenge with that is, if the people who just copy and past that code, don't understand the implications of that code, we have a much higher risk continuously. And what people thought was, is challenging with Stack Overflow. Imagine that something in one of these AI engines, is actually going ballistic, and it creates holes in nearly every one of these applications. And trust me, there will be enough developers who are going to use these tools to develop codes, the same as students in university are going to take this to write their essays and everything else. And so it's really important that every developer team basically has a security person within their team, and not a security at the end. So we build something, we check it, go through QA, and then it goes to security. Security needs to be at the forefront. And I think that's where we see Cloud Native Security Con, where we see AWS. I saw it during re:Invent already where they said is like, we have reinforced next year. I think this becomes more and more of a topic, and I think companies, as much as it is become a norm that you have a firewall and everything else, it needs to become a norm that when you are doing software development, and every development team needs to have a security person on that needs to be trained. >> I love that chat comment Dave, 'cause you and I were talking about this. And I think that is going to be the issue. Do we need security chat for the chat bot? And there's like a, like a recursive model there. The biases are built in. I think, and I think our interview with the Palo Alto Network's co-founder, Dave, when he talked about zero trust as a structured way to start things, but he was referencing that with Cloud, there's a chance to rethink or do a do-over in security. So, I think this is kind of to me, where this is all going. And I think you asked Pat Gelsinger what, year 2013, 2014, can, is security a do over? I think we're in that do over time. >> He said yes. >> He said yes. (laughing) He was right. But yeah, eight years later... But this is, how do you, zero trust gives you some structure, but how do you organize and redo security? Because to me, I think that's what's happening here. >> And John you heard, Zuk at Palo Alto Network said, "Yeah, the, the words security and architecture, they don't go together historically." And so it is a total, total retake. >> Well is that because there's too many tools out there and- >> Yeah. For sure. >> Yeah, well, first of all, a lot of hardware. And then yeah, a lot of tools. You even see IIOT and industry 40, you see IOT security coming up as another stove pipe, and that's not the right approach. And, and so- >> Well let me, let me ask you a question Dave, and Yves, if you don't mind. 'Cause I was just riffing on this yesterday about this. In the ML space, you're seeing the ML models, you're seeing proprietary models versus open source. Is security going to go down this proprietary security methods and open source? Because that's interesting, because the CNCF is run by the the Linux Foundation. So you can almost maybe see a model where there's more proprietary security methods than open source. Or is it, is that a non-issue? >> I would, I would, let me, if I, if I jump in here first, I think the last, especially last five or 10 years have clearly shown the, the whole and, and I invested early on in the, in the end 90s in several open source startups in the Bay area. So, I'm well behind the whole open source idea and, and mid (indistinct) and others back then several times. But the point is, I think what we have seen is open source is not in general, more secure or less secure, because code is too complex nowadays. You have millions of lines of code, and it's not that either one way or the other is going to solve it. The ways I think we are going to look at it is more is what's the role to market, because only because something is open source doesn't necessarily mean it's going to be available for everyone. And the same for proprietary source from that perspective, even though everybody mixes licensing and payments and all that all the time, but it doesn't necessarily have anything to do with it. But I think as we are going through it, and when we also look at the industry, security industry over the last 10 plus years has been primarily hardware focused. And a lot of these vendors have done a good business out of selling hardware boxes, putting software on top of it. Whereas in reality, those were still X86 standard boxes in the end. So it was not that we had specific security ethics or anything like that in there anymore. And so overall, the question of the market is going to change. And as we are looking into Cloud Native, think about someone like an AWS, do you really envision them to have a hardware box of every supplier in their data center, and that in every availability zone in every region? Same for Microsoft, same for Google, etc? So we need to have new ways on how we can apply security. And that applies both on the backend services, but also on the front end side. >> And if I, and if I could chime in, I think the, the good, I think the answer is, is, is no and yes. And what I mean by that is if you take, antivirus and known malware, I mean pretty much anybody today can, can solve that problem, it's the unknown malware. So I think the yes part of the answer is yes, it's, it's going to be proprietary, but in the sense we're going to use open source tooling, and then apply that in a proprietary way with, with specific algorithms and unique architectures that are going to solve problems. For example, XDR with, with unknown malware. So, and that's the, that's the hard part. As somebody said, I think this morning at the keynote, it's, it's all the stuff that, that the SecOps team couldn't find. That's the really hard part. >> (laughs) Well the question will be will, is the new IP, the ability to feed ChatGPT some magical spelled insertion query string that does the job, that's unique, that might be the new IP, the the question to ask. >> Well, that's what the hackers are going to do. And I, they're on offense. (John laughs) And the offense knows what play is coming. So, they're going to start. >> So guys, let's take this conversation up a level. I want to get your perspectives on what's in this for me as a customer? We know security is a board level conversation. We talk about this all the time. We also know that they're based on, I think David, was the conversations that you and I had, with Palo Alto Networks at Ignite in December. There's a, there's a lack of alignment between the executives and the board from a security perspective. When we talk about Cloud Native Security, we all talked about the value in that, what's in it for customers? I want to get your perspectives on should this be a board level conversation, and if so, how do you advise organizations, whether it is a hospital, or a bank, or an organization that is really affected by things like ransomware? How should they be thinking about this from an organizational perspective? >> Well, I'll start first, because we had this conversation during our Super Cloud event last month, and this comes up a lot. And this is, the CEO board level. Yes it is a board level conversation for security, as is application development as in terms of transforming their business to be competitive, not to be on the wrong side of history with this wave coming. So I think that's more of a management. But the issue is, they tell their people, "Go do it." And they're like, 'cause they get sold on the idea of, "Hey, won't you transform your business, and everything's going to be data driven, and machine learning's going to power your apps, get new customers, be profitable." "Oh, sign me up for that." When you have to implement this, it's really hard. And I think the core issue is, where are companies in their life cycle of the ability to execute and architect this thing properly as Dave said, Nick Zuk said, "You can't have architecture and security, you need platforms." So, I think the re-platforming, and the re-factoring of business is a big factor, and that's got to get down into the, the organizational shifts and the people to do it. So are there skills? Do I do a managed service? How do I architect it? Are there more services? Are there developers doing applications that are going to be more agile? So, this is not an easy thing. And to move a business from IT operations that is proven, to be positioned for this enablement, is just really difficult. And it's expensive. And if you screw it up, you could be, could be on the wrong side of things. So, to me, that's the big issue is, you sell the dream and then you got to implement it. And that's really difficult. >> Yves, give us your perspective on, based on John's comments, how do organizations shift so dramatically? There's a cultural element there as well, but there's also organizations that are, have competitive competitors in the rear view mirror, and there's time to waste. What are your thoughts on that? >> I think that's exactly the point. It's like, as an organization, you need to take the decision between the time, the risk, and all the other elements we have into this game. Because you can try to achieve 100% security, but that's exactly the same as trying to, to protect gold or anything else 100%. It's most likely not going to be from a risk perspective anyway sensible. And that's the same from a corporational perspective. When you look at building new internet services, or IOT services, or any kind of new shopping experience or whatever else, you need to balance out between the risks and the advantages out of it. And you also need to be accepting that you potentially on the way make mistakes, but then it's more important than ever that you are able to quickly fix any mistakes, and to adjust to anything what's happening in the market. Because as we are building all these new Cloud Native applications, and build up all these skill sets, one of the big scenarios is we are far more depending on individual building blocks. These building blocks come out of open source communities, which have a much different way. When we look back in software development, back then we had application servers from Oracle, Web Logic, whatsoever, they had a release cycles of every three to six months. As now we have to deal with open source, where sometimes release cycles are on a four week schedule, in between security patches. So you need to be much faster in adopting that, checking that, implementing that, getting things to work. So there is a security stretch from that perspective. There is a speech stretch on the other thing companies have to deal with, and on the other side it's always a measurement between the risk, and the security you can afford. Because reality is, you will not be 100% protected no matter what you do. So, you need to balance out what you as an organization can actually build on. But I think, coming back also to the point, it's on the bot level nowadays. It's like nearly every discussion we have with companies nowadays as they move into the Cloud, especially also here in Europe where for the last five years, it was always, it's like "It's data privacy." Data privacy is no longer, I mean, yes, for certain people, it's still the point, but for many more people it's like, "How protected is my data?" "What do we do in case of ransomware attack?" "What do we do in case of a denial of service?" All of these things become more vulnerable, where in the past you were discussing these things with a becking page, or, or like a stock exchange. They were, it's like, "What the hell is going to happen if we have a denial of service?" Now all of the sudden, this now affects nearly everyone in their storefronts and everything else, because everything is depending on it. >> Yeah, I think you're right on. You think about how cultural change occurs, it's bottom ups or, bottom up, top down or middle out. And what, what's happened with security is the people in the security team cared about it, they were the, everybody said, "Oh, it's their problem." And then it just did an end run to the board, kind of mid, early last decade. And then the board sort of pushed that down. And the line of business is realizing, "Holy cow. My business, my EBIT can be dramatically affected by this, so I care." Now it's this whole house, cultural team sport. I know it's sort of a, a cliche, but it, it's true. Everybody actually is beginning to care about security because the risks are now so high, and it's going to affect not only the bottom line of the company, the bottom line of the business, their job, it's, it's, it's virtually everywhere. It's a huge cultural shift that we're seeing. >> And that's a big challenge for organizations in any industry. And Yves, you talked about ransomware service. Every industry across the globe is vulnerable to this. But how can, maybe John, we'll start with you. How can Cloud Native Security help organizations if they're able to embrace it, operationally, culturally, dial down some of the vulnerabilities that just seem to keep growing? >> Well, I mean that's the big question. The breaches are, are critical. The governances also could be a way that anchors down growth. So I think the balance between the governance compliance piece of it is key, but making the developers faster and more productive is the key to me. And I think having the security paradigm where they're not blockers, as Dave said, is critical. So I love the whole shift left, but now that we have more data focused initiatives around how that, you can use data to understand the security issues, I think data and security are together, and I think there's a going to be a data operating system model emerging, where data and security will be almost one thing. And that will be set up by the security teams, and the data teams together. And that will feed guardrails into the developer environment. So the developer should feel no pain at all in doing this. So I think the best practice will end up being what we're seeing with supply chain, security, with making sure code's verified. And you're going to see the container, security side completely address has been, and KubeCon, we just, I asked Scott Johnson, the CEO of Docker, and I asked him directly, "Are you guys all tight on container security?" He said, yes, but other people are suggesting that's not true. There's a lot of issues with the container security. So, there's all kinds of areas where there's holes. So Cloud Native is cool on one hand, and very relevant, but if it's not shored up, it's going to be a problem. But I, so I think that's where the action will be, at the developer pipeline, in the containers, and the data. So, that will be very relevant, and if companies nail that, they'll be faster, they'll have better apps, and that'll be the differentiator. And again, if they don't on this next wave, they're going to be driftwood. >> Dave, how do they prevent becoming driftwood? >> Well, I think Cloud has had a huge impact. And a Cloud's by no means a panacea, but let's face it, it's dramatically improved a lot of companies security posture. Now there's still that shared responsibility. Even though an S3 bucket is encrypted, it's still your responsibility to make sure that it doesn't get decrypted by somebody who has access to it. So there are things like that, but to Yve's earlier point, that can be, that's done through software now, it's done through best practices. Those best practices can be shared. So the way you, you don't become driftwood, is you start to, you step back, rethink that security architecture as we were talking about earlier, take advantage of the Cloud, take advantage of Cloud Native, and all the, the rapid pace of innovation that's occurring there, and you don't use, it's called before, The audit is the last line of defense. That's no longer a check box item. "Oh yeah, we're in compliance." It's, this is a business imperative, and because we're going to reduce our expected loss and reduce our business risk. That's part of the business case today. >> Yeah. >> It's a huge, critically important part of the business case. Yves, question for you. If you're in an elevator with a CEO, a CFO, and a CISO, and they're talking about security and Cloud Native Security, what's your value proposition to them on a, on a say a 32nd elevator ride? >> Difficult story. I think at the moment, the most important part is, we need to get people to work together, and we need to train people to work more much better together. I think that's the overall most important part for all of these solutions, because in the end, security is always a person issue. If, we can have the best tools in the industry, as long as we don't get all of these teams to work together, then we have a problem. If the security team is always seen as the end of the solution to fix everything, that's not going to work because they always are the bad guys in the game. And so we need to bring the teams together. And once we have the teams work together, I think we have a far better track on, on maintaining security. >> John and Dave, I want to get your perspectives on what Yves just said. In all the experience that the two of you have as industry analysts here on "theCUBE," Wikibon, Siliconangle Media. How do you advise organizations to get those teams together? As Eve said, that alignment is critical, but John, we'll start with you, then Dave go to you. What's your advice for organizations that need to align those teams and really don't have a lot of time to wait to do it? >> (chuckling) That's a great question. I think, I think that's everyone pays hundreds of thousands of millions of dollars to get that advice from these consultants, organizations out there doing the transformations. But I think it comes down to personnel and commitment. I think if there's a C-level commitment to the effort, you'll see the institutional structure change. So you can see really getting behind it with their, with their wallet and their, and their support of either getting more personnel to support and assist, or manage services, or giving the power to the teams to execute and doing it in a way that, that's, that's well known and best practices. Start small, build out the pilots, build the platform, and then start getting it right. And I think that's the key. Not the magic wand, the old model of rolling out stuff in, in six month cycles. It's really, get the proof points, double down and change the culture, but also execute and have real metrics. And changing the architecture, like having more penetration tests as a service. Doing pen tests is like a joke now. So that doesn't make any sense. You got to have that built in almost every day, and every minute. So, these kinds of new techniques have to be implemented and have to be tried. So that's why these communities are growing. That's why I like what open source has been doing, and I like the open source as the place to have these conversations, because that's where the action will be for new stuff. And I think people will implement open source like they did before, but with different ways, better testing, better supply chain on the software side, verifying code. So, I see open source actually getting a tailwind from this, not a headwind. So, I'm bullish on the open source piece here on, on all levels, machine learning- >> Lisa, my answer is intramural sports. And it's 'cause I think it's cultural. And what I mean by that, is you take your your best and brightest security, and this is what frankly, a lot of CISOs do, an examples is Lena Smart, MongoDB. Take your best and brightest security pros, make them captains of the intramural teams, and pair them up with pods of individuals across the organization, which is most people who don't know anything about security, and put them together, so that they can, they, so that the folks that understand security can, can realize how little people know, what, what, what, how, what the worst practices that are out there in the reverse, how they can cross pollinate. And they do that on a regular basis, I know at Mongo and other companies. And that kind of cultural assimilation is a starting point for how you get security awareness up to your question around making it a team sport. >> Absolutely critical. Yves, I want to kind of wrap things with you. We've got a couple of minutes left. When you're really looking at the Cloud Native community, the growth of it, we talked about earlier in the program, Cloud Native Security Con being now extracted and elevated out of KubeCon, what are your thoughts on the groundswell that this community is generating around Cloud Native Security, the benefits that organizations will achieve from it? >> I think overall, when we have these securities conferences, or these security arms a bit spread out and separated out of the main conference, it helps to a certain degree, because especially in the security space, when you look at at other like black hat or white hat conferences and things like that in the past, although they were not focused on Cloud Native, a lot of these security folks didn't feel well taken care of in any of the other conferences because they were always these, it's like they are always blocking us, they're always making us problems, and all these kinds of things. Now that we really take the Cloud Native piece and the security piece together, or like AWS does it with re:Inforce, I think we will see more and more that people understand is that security is a permanent topic we need to cover, but we need to bring different people together, because security also has compliance and a lot of other components in there. So we will see at these conferences moving forward, also a different audience. It's not going to be only the Cloud Native developers. And if I see some of these security audiences, I can't really imagine them to really be at KubeCon because there is too much other things going on. And you couldn't really see much of that at re:Invent because re:Invent by itself has become a complete monster of a conference. It covers too many topics. And so having this very, very important security piece separated, also gives the opportunity, I think, that we can bring in the security people, but also have the type of board level discussions potentially, between the leaders of the industry, to also discuss on how we can evolve, how we can make things better, and how, how we can actually, yeah, evolve our industry for it. Because let's face it, that threat is not going to go away. It's, it's a business. And one of the last security conferences I was on, on the ransomware part, it was one of the topics someone said is like, "Look, currently on average, it takes a hacker group roughly around they said 15 to 20 K to break into a company, and they on average make 100K. It's a business, let's face it. And it's a business we don't like. And ethically, it's no discussion that this is not good, but that's something which is happening. People are making money with it. And as long as that's going to go on, and we have enough countries where these people can hide, it's going to stay and survive. And so, with that being said, it's important for us to really build an industry around this. But I also think it's good that we have separate conferences. In the past we had more the RSA conference, which tried to cover all of these areas. But that is not really fitting Cloud Native and everything else. So I think it's good that we have these new opportunities, the Cloud Native one, but also what AWS brings up for someone. >> Yves, you just nailed it. It just comes down to simple math. It's a fraction. Revenue over cost. And if you could increase the hacker's cost, increase the denominator, their ROI will go down. And that is the game. >> Great point, Dave. What I'm hearing guys, and we can talk about technology for days and days. I know all of you. But there's, there's a big component that, that the elevation of Cloud Native Security, on its own as standalone is critical, as is the people component. You guys all talked about that. We talked about the cultural change necessary for that. Hopefully what we're seeing with Cloud Native Security Con 23, this first event is going to give us more insight over the next couple of days, and the next months or so, as to how this elevation, and how the people can come together to really help organizations from a math perspective as, as Dave talked about, really dial down the risks there, understand more of the vulnerabilities so that ransomware as a service is not as lucrative as it is today. Guys, so much appreciate your time, really breaking down Cloud Native Security, the value in it from different perspectives, and what your thoughts are on where it's going. Thanks so much for your time. >> All right. Thanks. >> Thanks, Lisa. >> Thank you. >> Thanks, Yves. >> All right. For my guests, I'm Lisa Martin. You're watching theCUBE's day one coverage of Cloud Native Security Con 23. Thanks for watching. (rousing music)

>> From theCUBE Studios in Palo Alto in Boston, bringing you data-driven insights from theCUBE in ETR. This is Breaking Analysis with Dave Vellante. >> Snowflake is not going to grow into its valuation by stealing the croissant from the breakfast table of the on-prem data warehouse vendors. Look, even if snowflake got 100% of the data warehouse business, it wouldn't come close to justifying its market cap. Rather Snowflake has to create an entirely new market based on completely changing the way organizations think about monetizing data. Every organization I talk to says it wants to be, or many say they already are data-driven. why wouldn't you aspire to that goal? There's probably nothing more strategic than leveraging data to power your digital business and creating competitive advantage. But many businesses are failing, or I predict, will fail to create a true data-driven culture because they're relying on a flawed architectural model formed by decades of building centralized data platforms. Welcome everyone to this week's Wikibon Cube Insights powered by ETR. In this Breaking Analysis, I want to share some new thoughts and fresh ETR data on how organizations can transform their businesses through data by reinventing their data architectures. And I want to share our thoughts on why we think Snowflake is currently in a very strong position to lead this effort. Now, on November 17th, theCUBE is hosting the Snowflake Data Cloud Summit. Snowflake's ascendancy and its blockbuster IPO has been widely covered by us and many others. Now, since Snowflake went public, we've been inundated with outreach from investors, customers, and competitors that wanted to either better understand the opportunities or explain why their approach is better or different. And in this segment, ahead of Snowflake's big event, we want to share some of what we learned and how we see it. Now, theCUBE is getting paid to host this event, so I need you to know that, and you draw your own conclusions from my remarks. But neither Snowflake nor any other sponsor of theCUBE or client of SiliconANGLE Media has editorial influence over Breaking Analysis. The opinions here are mine, and I would encourage you to read my ethics statement in this regard. I want to talk about the failed data model. The problem is complex, I'm not debating that. Organizations have to integrate data and platforms with existing operational systems, many of which were developed decades ago. And as a culture and a set of processes that have been built around these systems, and they've been hardened over the years. This chart here tries to depict the progression of the monolithic data source, which, for me, began in the 1980s when Decision Support Systems or DSS promised to solve our data problems. The data warehouse became very popular and data marts sprung up all over the place. This created more proprietary stovepipes with data locked inside. The Enron collapse led to Sarbanes-Oxley. Now, this tightened up reporting. The requirements associated with that, it breathed new life into the data warehouse model. But it remained expensive and cumbersome, I've talked about that a lot, like a snake swallowing a basketball. The 2010s ushered in the big data movement, and Data Lakes emerged. With a dupe, we saw the idea of no schema online, where you put structured and unstructured data into a repository, and figure it all out on the read. What emerged was a fairly complex data pipeline that involved ingesting, cleaning, processing, analyzing, preparing, and ultimately serving data to the lines of business. And this is where we are today with very hyper specialized roles around data engineering, data quality, data science. There's lots of batch of processing going on, and Spark has emerged to improve the complexity associated with MapReduce, and it definitely helped improve the situation. We're also seeing attempts to blend in real time stream processing with the emergence of tools like Kafka and others. But I'll argue that in a strange way, these innovations actually compound the problem. And I want to discuss that because what they do is they heighten the need for more specialization, more fragmentation, and more stovepipes within the data life cycle. Now, in reality, and it pains me to say this, it's the outcome of the big data movement, as we sit here in 2020, that we've created thousands of complicated science projects that have once again failed to live up to the promise of rapid cost-effective time to insights. So, what will the 2020s bring? What's the next silver bullet? You hear terms like the lakehouse, which Databricks is trying to popularize. And I'm going to talk today about data mesh. These are other efforts they look to modernize datalakes and sometimes merge the best of data warehouse and second-generation systems into a new paradigm, that might unify batch and stream frameworks. And this definitely addresses some of the gaps, but in our view, still suffers from some of the underlying problems of previous generation data architectures. In other words, if the next gen data architecture is incremental, centralized, rigid, and primarily focuses on making the technology to get data in and out of the pipeline work, we predict it's going to fail to live up to expectations again. Rather, what we're envisioning is an architecture based on the principles of distributed data, where domain knowledge is the primary target citizen, and data is not seen as a by-product, i.e, the exhaust of an operational system, but rather as a service that can be delivered in multiple forms and use cases across an ecosystem. This is why we often say the data is not the new oil. We don't like that phrase. A specific gallon of oil can either fuel my home or can lubricate my car engine, but it can't do both. Data does not follow the same laws of scarcity like natural resources. Again, what we're envisioning is a rethinking of the data pipeline and the associated cultures to put data needs of the domain owner at the core and provide automated, governed, and secure access to data as a service at scale. Now, how is this different? Let's take a look and unpack the data pipeline today and look deeper into the situation. You all know this picture that I'm showing. There's nothing really new here. The data comes from inside and outside the enterprise. It gets processed, cleanse or augmented so that it can be trusted and made useful. Nobody wants to use data that they can't trust. And then we can add machine intelligence and do more analysis, and finally deliver the data so that domain specific consumers can essentially build data products and services or reports and dashboards or content services, for instance, an insurance policy, a financial product, a loan, that these are packaged and made available for someone to make decisions on or to make a purchase. And all the metadata associated with this data is packaged along with the dataset. Now, we've broken down these steps into atomic components over time so we can optimize on each and make them as efficient as possible. And down below, you have these happy stick figures. Sometimes they're happy. But they're highly specialized individuals and they each do their job and they do it well to make sure that the data gets in, it gets processed and delivered in a timely manner. Now, while these individual pieces seemingly are autonomous and can be optimized and scaled, they're all encompassed within the centralized big data platform. And it's generally accepted that this platform is domain agnostic. Meaning the platform is the data owner, not the domain specific experts. Now there are a number of problems with this model. The first, while it's fine for organizations with smaller number of domains, organizations with a large number of data sources and complex domain structures, they struggle to create a common data parlance, for example, in a data culture. Another problem is that, as the number of data sources grows, organizing and harmonizing them in a centralized platform becomes increasingly difficult, because the context of the domain and the line of business gets lost. Moreover, as ecosystems grow and you add more data, the processes associated with the centralized platform tend to get further genericized. They again lose that domain specific context. Wait (chuckling), there are more problems. Now, while in theory organizations are optimizing on the piece parts of the pipeline, the reality is, as the domain requires a change, for example, a new data source or an ecosystem partnership requires a change in access or processes that can benefit a domain consumer, the reality is the change is subservient to the dependencies and the need to synchronize across these discrete parts of the pipeline or actually, orthogonal to each of those parts. In other words, in actuality, the monolithic data platform itself remains the most granular part of the system. Now, when I complain about this faulty structure, some folks tell me this problem has been solved. That there are services that allow new data sources to really easily be added. A good example of this is Databricks Ingest, which is, it's an auto loader. And what it does is it simplifies the ingestion into the company's Delta Lake offering. And rather than centralizing in a data warehouse, which struggles to efficiently allow things like Machine Learning frameworks to be incorporated, this feature allows you to put all the data into a centralized datalake. More so the argument goes, that the problem that I see with this, is while the approach does definitely minimizes the complexities of adding new data sources, it still relies on this linear end-to-end process that slows down the introduction of data sources from the domain consumer beside of the pipeline. In other words, the domain experts still has to elbow her way into the front of the line or the pipeline, in this case, to get stuff done. And finally, the way we are organizing teams is a point of contention, and I believe is going to continue to cause problems down the road. Specifically, we've again, we've optimized on technology expertise, where for example, data engineers, well, really good at what they do, they're often removed from the operations of the business. Essentially, we created more silos and organized around technical expertise versus domain knowledge. As an example, a data team has to work with data that is delivered with very little domain specificity, and serves a variety of highly specialized consumption use cases. All right. I want to step back for a minute and talk about some of the problems that people bring up with Snowflake and then I'll relate it back to the basic premise here. As I said earlier, we've been hammered by dozens and dozens of data points, opinions, criticisms of Snowflake. And I'll share a few here. But I'll post a deeper technical analysis from a software engineer that I found to be fairly balanced. There's five Snowflake criticisms that I'll highlight. And there are many more, but here are some that I want to call out. Price transparency. I've had more than a few customers telling me they chose an alternative database because of the unpredictable nature of Snowflake's pricing model. Snowflake, as you probably know, prices based on consumption, just like AWS and other cloud providers. So just like AWS, for example, the bill at the end of the month is sometimes unpredictable. Is this a problem? Yes. But like AWS, I would say, "Kill me with that problem." Look, if users are creating value by using Snowflake, then that's good for the business. But clearly this is a sore point for some users, especially for procurement and finance, which don't like unpredictability. And Snowflake needs to do a better job communicating and managing this issue with tooling that can predict and help better manage costs. Next, workload manage or lack thereof. Look, if you want to isolate higher performance workloads with Snowflake, you just spin up a separate virtual warehouse. It's kind of a brute force approach. It works generally, but it will add expense. I'm kind of reminded of Pure Storage and its approach to storage management. The engineers at Pure, they always design for simplicity, and this is the approach that Snowflake is taking. Usually, Pure and Snowflake, as I have discussed in a moment, is Pure's ascendancy was really based largely on stealing share from Legacy EMC systems. Snowflake, in my view, has a much, much larger incremental market opportunity. Next is caching architecture. You hear this a lot. At the end of the day, Snowflake is based on a caching architecture. And a caching architecture has to be working for some time to optimize performance. Caches work well when the size of the working set is small. Caches generally don't work well when the working set is very, very large. In general, transactional databases have pretty small datasets. And in general, analytics datasets are potentially much larger. Is it Snowflake in the analytics business? Yes. But the good thing that Snowflake has done is they've enabled data sharing, and it's caching architecture serves its customers well because it allows domain experts, you're going to hear this a lot from me today, to isolate and analyze problems or go after opportunities based on tactical needs. That said, very big queries across whole datasets or badly written queries that scan the entire database are not the sweet spot for Snowflake. Another good example would be if you're doing a large audit and you need to analyze a huge, huge dataset. Snowflake's probably not the best solution. Complex joins, you hear this a lot. The working set of complex joins, by definition, are larger. So, see my previous explanation. Read only. Snowflake is pretty much optimized for read only data. Maybe stateless data is a better way of thinking about this. Heavily right intensive workloads are not the wheelhouse of Snowflake. So where this is maybe an issue is real-time decision-making and AI influencing. A number of times, Snowflake, I've talked about this, they might be able to develop products or acquire technology to address this opportunity. Now, I want to explain. These issues would be problematic if Snowflake were just a data warehouse vendor. If that were the case, this company, in my opinion, would hit a wall just like the NPP vendors that proceeded them by building a better mouse trap for certain use cases hit a wall. Rather, my promise in this episode is that the future of data architectures will be really to move away from large centralized warehouses or datalake models to a highly distributed data sharing system that puts power in the hands of domain experts at the line of business. Snowflake is less computationally efficient and less optimized for classic data warehouse work. But it's designed to serve the domain user much more effectively in our view. We believe that Snowflake is optimizing for business effectiveness, essentially. And as I said before, the company can probably do a better job at keeping passionate end users from breaking the bank. But as long as these end users are making money for their companies, I don't think this is going to be a problem. Let's look at the attributes of what we're proposing around this new architecture. We believe we'll see the emergence of a total flip of the centralized and monolithic big data systems that we've known for decades. In this architecture, data is owned by domain-specific business leaders, not technologists. Today, it's not much different in most organizations than it was 20 years ago. If I want to create something of value that requires data, I need to cajole, beg or bribe the technology and the data team to accommodate. The data consumers are subservient to the data pipeline. Whereas in the future, we see the pipeline as a second class citizen, with a domain expert is elevated. In other words, getting the technology and the components of the pipeline to be more efficient is not the key outcome. Rather, the time it takes to envision, create, and monetize a data service is the primary measure. The data teams are cross-functional and live inside the domain versus today's structure where the data team is largely disconnected from the domain consumer. Data in this model, as I said, is not the exhaust coming out of an operational system or an external source that is treated as generic and stuffed into a big data platform. Rather, it's a key ingredient of a service that is domain-driven and monetizable. And the target system is not a warehouse or a lake. It's a collection of connected domain-specific datasets that live in a global mesh. What is a distributed global data mesh? A data mesh is a decentralized architecture that is domain aware. The datasets in the system are purposely designed to support a data service or data product, if you prefer. The ownership of the data resides with the domain experts because they have the most detailed knowledge of the data requirement and its end use. Data in this global mesh is governed and secured, and every user in the mesh can have access to any dataset as long as it's governed according to the edicts of the organization. Now, in this model, the domain expert has access to a self-service and obstructed infrastructure layer that is supported by a cross-functional technology team. Again, the primary measure of success is the time it takes to conceive and deliver a data service that could be monetized. Now, by monetize, we mean a data product or data service that it either cuts cost, it drives revenue, it saves lives, whatever the mission is of the organization. The power of this model is it accelerates the creation of value by putting authority in the hands of those individuals who are closest to the customer and have the most intimate knowledge of how to monetize data. It reduces the diseconomies at scale of having a centralized or a monolithic data architecture. And it scales much better than legacy approaches because the atomic unit is a data domain, not a monolithic warehouse or a lake. Zhamak Dehghani is a software engineer who is attempting to popularize the concept of a global mesh. Her work is outstanding, and it's strengthened our belief that practitioners see this the same way that we do. And to paraphrase her view, "A domain centric system must be secure and governed with standard policies across domains." It has to be trusted. As I said, nobody's going to use data they don't trust. It's got to be discoverable via a data catalog with rich metadata. The data sets have to be self-describing and designed for self-service. Accessibility for all users is crucial as is interoperability, without which distributed systems, as we know, fail. So what does this all have to do with Snowflake? As I said, Snowflake is not just a data warehouse. In our view, it's always had the potential to be more. Our assessment is that attacking the data warehouse use cases, it gave Snowflake a straightforward easy-to-understand narrative that allowed it to get a foothold in the market. Data warehouses are notoriously expensive, cumbersome, and resource intensive, but they're a critical aspect to reporting and analytics. So it was logical for Snowflake to target on-premise legacy data warehouses and their smaller cousins, the datalakes, as early use cases. By putting forth and demonstrating a simple data warehouse alternative that can be spun up quickly, Snowflake was able to gain traction, demonstrate repeatability, and attract the capital necessary to scale to its vision. This chart shows the three layers of Snowflake's architecture that have been well-documented. The separation of compute and storage, and the outer layer of cloud services. But I want to call your attention to the bottom part of the chart, the so-called Cloud Agnostic Layer that Snowflake introduced in 2018. This layer is somewhat misunderstood. Not only did Snowflake make its Cloud-native database compatible to run on AWS than Azure in the 2020 GCP, what Snowflake has done is to obstruct cloud infrastructure complexity and create what it calls the data cloud. What's the data cloud? We don't believe the data cloud is just a marketing term that doesn't have any substance. Just as SAS is Simplified Application Software and iOS made it possible to eliminate the value drain associated with provisioning infrastructure, a data cloud, in concept, can simplify data access, and break down fragmentation and enable shared data across the globe. Snowflake, they have a first mover advantage in this space, and we see a number of fundamental aspects that comprise a data cloud. First, massive scale with virtually unlimited compute and storage resource that are enabled by the public cloud. We talk about this a lot. Second is a data or database architecture that's built to take advantage of native public cloud services. This is why Frank Slootman says, "We've burned the boats. We're not ever doing on-prem. We're all in on cloud and cloud native." Third is an obstruction layer that hides the complexity of infrastructure. and fourth is a governed and secured shared access system where any user in the system, if allowed, can get access to any data in the cloud. So a key enabler of the data cloud is this thing called the global data mesh. Now, earlier this year, Snowflake introduced its global data mesh. Over the course of its recent history, Snowflake has been building out its data cloud by creating data regions, strategically tapping key locations of AWS regions and then adding Azure and GCP. The complexity of the underlying cloud infrastructure has been stripped away to enable self-service, and any Snowflake user becomes part of this global mesh, independent of the cloud that they're on. Okay. So now, let's go back to what we were talking about earlier. Users in this mesh will be our domain owners. They're building monetizable services and products around data. They're most likely dealing with relatively small read only datasets. They can adjust data from any source very easily and quickly set up security and governance to enable data sharing across different parts of an organization, or, very importantly, an ecosystem. Access control and governance is automated. The data sets are addressable. The data owners have clearly defined missions and they own the data through the life cycle. Data that is specific and purposely shaped for their missions. Now, you're probably asking, "What happens to the technical team and the underlying infrastructure and the cluster it's in? How do I get the compute close to the data? And what about data sovereignty and the physical storage later, and the costs?" All these are good questions, and I'm not saying these are trivial. But the answer is these are implementation details that are pushed to a self-service layer managed by a group of engineers that serves the data owners. And as long as the domain expert/data owner is driving monetization, this piece of the puzzle becomes self-funding. As I said before, Snowflake has to help these users to optimize their spend with predictive tooling that aligns spend with value and shows ROI. While there may not be a strong motivation for Snowflake to do this, my belief is that they'd better get good at it or someone else will do it for them and steal their ideas. All right. Let me end with some ETR data to show you just how Snowflake is getting a foothold on the market. Followers of this program know that ETR uses a consistent methodology to go to its practitioner base, its buyer base each quarter and ask them a series of questions. They focus on the areas that the technology buyer is most familiar with, and they ask a series of questions to determine the spending momentum around a company within a specific domain. This chart shows one of my favorite examples. It shows data from the October ETR survey of 1,438 respondents. And it isolates on the data warehouse and database sector. I know I just got through telling you that the world is going to change and Snowflake's not a data warehouse vendor, but there's no construct today in the ETR dataset to cut a data cloud or globally distributed data mesh. So you're going to have to deal with this. What this chart shows is net score in the y-axis. That's a measure of spending velocity, and it's calculated by asking customers, "Are you spending more or less on a particular platform?" And then subtracting the lesses from the mores. It's more granular than that, but that's the basic concept. Now, on the x-axis is market share, which is ETR's measure of pervasiveness in the survey. You can see superimposed in the upper right-hand corner, a table that shows the net score and the shared N for each company. Now, shared N is the number of mentions in the dataset within, in this case, the data warehousing sector. Snowflake, once again, leads all players with a 75% net score. This is a very elevated number and is higher than that of all other players, including the big cloud companies. Now, we've been tracking this for a while, and Snowflake is holding firm on both dimensions. When Snowflake first hit the dataset, it was in the single digits along the horizontal axis and continues to creep to the right as it adds more customers. Now, here's another chart. I call it the wheel chart that breaks down the components of Snowflake's net score or spending momentum. The lime green is new adoption, the forest green is customers spending more than 5%, the gray is flat spend, the pink is declining by more than 5%, and the bright red is retiring the platform. So you can see the trend. It's all momentum for this company. Now, what Snowflake has done is they grabbed a hold of the market by simplifying data warehouse. But the strategic aspect of that is that it enables the data cloud leveraging the global mesh concept. And the company has introduced a data marketplace to facilitate data sharing across ecosystems. This is all about network effects. In the mid to late 1990s, as the internet was being built out, I worked at IDG with Bob Metcalfe, who was the publisher of InfoWorld. During that time, we'd go on speaking tours all over the world, and I would listen very carefully as he applied Metcalfe's law to the internet. Metcalfe's law states that the value of the network is proportional to the square of the number of connected nodes or users on that system. Said another way, while the cost of adding new nodes to a network scales linearly, the consequent value scores scales exponentially. Now, apply that to the data cloud. The marginal cost of adding a user is negligible, practically zero, but the value of being able to access any dataset in the cloud... Well, let me just say this. There's no limitation to the magnitude of the market. My prediction is that this idea of a global mesh will completely change the way leading companies structure their businesses and, particularly, their data architectures. It will be the technologists that serve domain specialists as it should be. Okay. Well, what do you think? DM me @dvellante or email me at david.vellante@siliconangle.com or comment on my LinkedIn? Remember, these episodes are all available as podcasts, so please subscribe wherever you listen. I publish weekly on wikibon.com and siliconangle.com, and don't forget to check out etr.plus for all the survey analysis. This is Dave Vellante for theCUBE Insights powered by ETR. Thanks for watching. Be well, and we'll see you next time. (upbeat music)

>> Announcer: From around the globe, it's theCUBE with digital coverage of MIT Chief Data Officer and Information Quality symposium brought to you by SiliconANGLE Media. >> Hi everybody. This is Dave Vellante and welcome back to theCUBE's coverage of the MIT CDOIQ 2020 event. Of course, it's gone virtual. We wish we were all together in Cambridge. They were going to move into a new building this year for years they've done this event at the Tang Center, moving into a new facility, but unfortunately going to have to wait at least a year, we'll see, But we've got a great guest. Nonetheless, Doug Laney is here. He's a Business Value Strategist, the bestselling author, an analyst, consultant then a long time CUBE friend. Doug, great to see you again. Thanks so much for coming on. >> Dave, great to be with you again as well. So can I ask you? You have been an advocate for obviously measuring the value of data, the CDO role. I don't take this the wrong way, but I feel like the last 150 days have done more to accelerate people's attention on the importance of data and the value of data than all the great work that you've done. What do you think? (laughing) >> It's always great when organizations, actually take advantage of some of these concepts of data value. You may be speaking specifically about the situation with United Airlines and American Airlines, where they have basically collateralized their customer loyalty data, their customer loyalty programs to the tunes of several billion dollars each. And one of the things that's very interesting about that is that the third party valuations of their customer loyalty data, resulted in numbers that were larger than the companies themselves. So basically the value of their data, which is as we've discussed previously off balance sheet is more valuable than the market cap of those companies themselves, which is just incredibly fascinating. >> Well, and of course, all you have to do is look to the Trillionaire's Club. And now of course, Apple pushing two trillion to really see the value that the market places on data. But the other thing is of course, COVID, everybody talks about the COVID acceleration. How have you seen it impact the awareness of the importance of data, whether it applies to business resiliency or even new monetization models? If you're not digital, you can't do business. And digital is all about data. >> I think the major challenge that most organizations are seeing from a data and analytics perspective due to COVID is that their traditional trend based forecast models are broken. If you're a company that's only forecasting based on your own historical data and not taking into consideration, or even identifying what are the leading indicators of your business, then COVID and the economic shutdown have entirely broken those models. So it's raised the awareness of companies to say, "Hey, how can we predict our business now? We can't do it based on our own historical data. We need to look externally at what are those external, maybe global indicators or other kinds of markets that proceed our own forecasts or our own activity." And so the conversion from trend based forecast models to what we call driver based forecast models, isn't easy for a lot of organizations to do. And one of the more difficult parts is identifying what are those external data factors from suppliers, from customers, from partners, from competitors, from complimentary products and services that are leading indicators of your business. And then recasting those models and executing on them. >> And that's a great point. If you think about COVID and how it's changed things, everything's changed, right? The ideal customer profile has changed, your value proposition to those customers has completely changed. You got to rethink that. And of course, it's very hard to predict even when this thing eventually comes back, some kind of hybrid mode, you used to be selling to people in an office environment. That's obviously changed. There's a lot that's permanent there. And data is potentially at least the forward indicator, the canary in the coal mine. >> Right. It also is the product and service. So not only can it help you and improve your forecasting models, but it can become a product or service that you're offering. Look at us right now, we would generally be face to face and person to person, but we're using video technology to transfer this content. And then one of the things that I... It took me awhile to realize, but a couple of months after the COVID shutdown, it occurred to me that even as a consulting organization, Caserta focuses on North America. But the reality is that every consultancy is now a global consultancy because we're all doing business remotely. There are no particular or real strong localization issues for doing consulting today. >> So we talked a lot over the years about the role of the CDO, how it's evolved, how it's changed the course of the early... The pre-title days it was coming out of a data quality world. And it's still vital. Of course, as we heard today from the Keynote, it's much more public, much more exposed, different public data sources, but the role has certainly evolved initially into regulated industries like financial, healthcare and government, but now, many, many more organizations have a CDO. My understanding is that you're giving a talk in the business case for the CDO. Help us understand that. >> Yeah. So one of the things that we've been doing here for the last couple of years is a running an ongoing study of how organizations are impacted by the role of the CDO. And really it's more of a correlation and looking at what are some of the qualities of organizations that have a CDO or don't have a CDO. So some of the things we found is that organizations with a CDO nearly twice as often, mention the importance of data and analytics in their annual report organizations with a C level CDO, meaning a true executive are four times more often likely to be using data, to transform the business. And when we're talking about using data and advanced analytics, we found that organizations with a CIO, not a CDO responsible for their data assets are only half as likely to be doing advanced analytics in any way. So there are a number of interesting things that we found about companies that have a CDO and how they operate a bit differently. >> I want to ask you about that. You mentioned the CIO and we're increasingly seeing lines of reporting and peer reporting alter shift. The sands are shifting a little bit. In the early days the CDO and still predominantly I think is an independent organization. We've seen a few cases and increasingly number where they're reporting into the CIO, we've seen the same thing by the way with the chief Information Security Officer, which used to be considered the fox watching the hen house. So we're seeing those shifts. We've also seen the CDO become more aligned with a technical role and sometimes even emerging out of that technical role. >> Yeah. I think the... I don't know, what I've seen more is that the CDOs are emerging from the business, companies are realizing that data is a business asset. It's not an IT asset. There was a time when data was tightly coupled with applications of technologies, but today data is very easily decoupled from those applications and usable in a wider variety of contexts. And for that reason, as data gets recognized as a business, not an IT asset, you want somebody from the business responsible for overseeing that asset. Yes, a lot of CDOs still report to the CIO, but increasingly more CDOs you're seeing and I think you'll see some other surveys from other organizations this week where the CDOs are more frequently reporting up to the CEO level, meaning they're true executives. Along I advocated for the bifurcation of the IT organization into separate I and T organizations. Again, there's no reason other than for historical purposes to keep the data and technology sides of the organizations so intertwined. >> Well, it makes sense that the Chief Data Officer would have an affinity with the lines of business. And you're seeing a lot of organizations, really trying to streamline their data pipeline, their data life cycles, bringing that together, infuse intelligence into that, but also take a systems view and really have the business be intimately involved, if not even owned into the data. You see a lot of emphasis on self-serve, what are you seeing in terms of that data pipeline or the data life cycle, if you will, that used to be wonky, hard core techies, but now it really involving a lot more constituent. >> Yeah. Well, the data life cycle used to be somewhat short. The data life cycles, they're longer and they're more a data networks than a life cycle and or a supply chain. And the reason is that companies are finding alternative uses for their data, not just using it for a single operational purpose or perhaps reporting purpose, but finding that there are new value streams that can be generated from data. There are value streams that can be generated internally. There are a variety of value streams that can be generated externally. So we work with companies to identify what are those variety of value streams? And then test their feasibility, are they ethically feasible? Are they legally feasible? Are they economically feasible? Can they scale? Do you have the technology capabilities? And so we'll run through a process of assessing the ideas that are generated. But the bottom line is that companies are realizing that data is an asset. It needs to be not just measured as one and managed as one, but also monetized as an asset. And as we've talked about previously, data has these unique qualities that it can be used over and over again, and it generate more data when you use it. And it can be used simultaneously for multiple purposes. So companies like, you mentioned, Apple and others have built business models, based on these unique qualities of data. But I think it's really incumbent upon any organization today to do so as well. >> But when you observed those companies that we talk about all the time, data is at the center of their organization. They maybe put people around that data. That's got to be one of the challenge for many of the incumbents is if we talked about the data silos, the different standards, different data quality, that's got to be fairly major blocker for people becoming a "Data-driven organization." >> It is because some organizations were developed as people driven product, driven brand driven, or other things to try to convert. To becoming data-driven, takes a high degree of data literacy or fluency. And I think there'll be a lot of talk about that this week. I'll certainly mention it as well. And so getting the organization to become data fluent and appreciate data as an asset and understand its possibilities and the art of the possible with data, it's a long road. So the culture change that goes along with it is really difficult. And so we're working with 150 year old consumer brand right now that wants to become more data-driven and they're very product driven. And we hear the CIO say, "We want people to understand that we're a data company that just happens to produce this product. We're not a product company that generates data." And once we realized that and started behaving in that fashion, then we'll be able to really win and thrive in our marketplace. >> So one of the key roles of a Chief Data Officers to understand how data affects the monetization of an organization. Obviously there are four profit companies of your healthcare organization saving lives, obviously being profitable as well, or at least staying within the budget, depending upon the structure of the organization. But a lot of people I think oftentimes misunderstand that it's like, "Okay, do I have to become a data broker? Am I selling data directly?" But I think, you pointed out many times and you just did that unlike oil, that's why we don't like that data as a new oil analogy, because it's so much more valuable and can be use, it doesn't fall because of its scarcity. But what are you finding just in terms of people's application of that notion of monetization? Cutting costs, increasing revenue, what are you seeing in the field? What's that spectrum look like? >> So one of the things I've done over the years is compile a library of hundreds and hundreds of examples of how organizations are using data and analytics in innovative ways. And I have a book in process that hopefully will be out this fall. I'm sharing a number of those inspirational examples. So that's the thing that organizations need to understand is that there are a variety of great examples out there, and they shouldn't just necessarily look to their own industry. There are inspirational examples from other industries as well, many clients come to me and they ask, "What are others in my industry doing?" And my flippant response to that is, "Why do you want to be in second place or third place? Why not take an idea from another industry, perhaps a digital product company and apply that to your own business." But like you mentioned, there are a variety of ways to monetize data. It doesn't involve necessarily selling it. You can deliver analytics, you can report on it, you can use it internally to generate improved business process performance. And as long as you're measuring how data's being applied and what its impact is, then you're in a position to claim that you're monetizing it. But if you're not measuring the impact of data on business processes or on customer relationships or partner supplier relationships or anything else, then it's difficult to claim that you're monetizing it. But one of the more interesting ways that we've been working with organizations to monetize their data, certainly in light of GDPR and the California consumer privacy act where I can't sell you my data anymore, but we've identified ways to monetize your customer data in a couple of ways. One is to synthesize the data, create synthetic data sets that retain the original statistical anomalies in the data or features of the data, but don't share actually any PII. But another interesting way that we've been working with organizations to monetize their data is what I call, Inverted data monetization, where again, I can't share my customer data with you, but I can share information about your products and services with my customers. And take a referral fee or a commission, based on that. So let's say I'm a hospital and I can't sell you my patient data, of course, due to variety of regulations, but I know who my diabetes patients are, and I can introduce them to your healthy meal plans, to your gym memberships, to your at home glucose monitoring kits. And again, take a referral fee or a cut of that action. So we're working with customers and the financial services firm industry and in the healthcare industry on just those kinds of examples. So we've identified hundreds of millions of dollars of incremental value for organizations that from their data that we're just sitting on. >> Interesting. Doug because you're a business value strategist at the top, where in the S curve do you see you're able to have the biggest impact. I doubt that you enter organizations where you say, "Oh, they've got it all figured out. They can't use my advice." But as well, sometimes in the early stages, you may not be able to have as big of an impact because there's not top down support or whatever, there's too much technical data, et cetera, where are you finding you can have the biggest impact, Doug? >> Generally we don't come in and run those kinds of data monetization or information innovation exercises, unless there's some degree of executive support. I've never done that at a lower level, but certainly there are lower level more immediate and vocational opportunities for data to deliver value through, to simply analytics. One of the simple examples I give is, I sold a home recently and when you put your house on the market, everybody comes out of the woodwork, the fly by night, mortgage companies, the moving companies, the box companies, the painters, the landscapers, all know you're moving because your data is in the U.S. and the MLS directory. And it was interesting. The only company that didn't reach out to me was my own bank, and so they lost the opportunity to introduce me to a Mortgage they'd retain me as a client, introduce me to my new branch, print me new checks, move the stuff in my safe deposit box, all of that. They missed a simple opportunity. And I'm thinking, this doesn't require rocket science to figure out which of your customers are moving, the MLS database or you can harvest it from Zillow or other sites is basically public domain data. And I was just thinking, how stupid simple would it have been for them to hire a high school programmer, give him a can of red bull and say, "Listen match our customer database to the MLS database to let us know who's moving on a daily or weekly basis." Some of these solutions are pretty simple. >> So is that part of what you do, come in with just hardcore tactical ideas like that? Are you also doing strategy? Tell me more about how you're spending your time. >> I trying to think more of a broader approach where we look at the data itself and again, people have said, "If you tortured enough, what would you tell us? We're just take that angle." We look at examples of how other organizations have monetized data and think about how to apply those and adapt those ideas to the company's own business. We look at key business drivers, internally and externally. We look at edge cases for their customers' businesses. We run through hypothesis generating activities. There are a variety of different kinds of activities that we do to generate ideas. And most of the time when we run these workshops, which last a week or two, we'll end up generating anywhere from 35 to 50 pretty solid ideas for generating new value streams from data. So when we talk about monetizing data, that's what we mean, generating new value streams. But like I said, then the next step is to go through that feasibility assessment and determining which of these ideas you actually want to pursue. >> So you're of course the longtime industry watcher as well, as a former Gartner Analyst, you have to be. My question is, if I think back... I've been around a while. If I think back at the peak of Microsoft's prominence in the PC era, it was like windows 95 and you felt like, "Wow, Microsoft is just so strong." And then of course the Linux comes along and a lot of open source changes and low and behold, a whole new set of leaders emerges. And you see the same thing today with the Trillionaire's Club and you feel like, "Wow, even COVID has been a tailwind for them." But you think about, "Okay, where could the disruption come to these large players that own huge clouds, they have all the data." Is data potentially a disruptor for what appear to be insurmountable odds against the newbies" >> There's always people coming up with new ways to leverage data or new sources of data to capture. So yeah, there's certainly not going to be around for forever, but it's been really fascinating to see the transformation of some companies I think nobody really exemplifies it more than IBM where they emerged from originally selling meat slicers. The Dayton Meat Slicer was their original product. And then they evolved into Manual Business Machines and then Electronic Business Machines. And then they dominated that. Then they dominated the mainframe software industry. Then they dominated the PC industry. Then they dominated the services industry to some degree. And so they're starting to get into data. And I think following that trajectory is something that really any organization should be looking at. When do you actually become a data company? Not just a product company or a service company or top. >> We have Inderpal Bhandari is one of our huge guests here. He's a Chief-- >> Sure. >> Data Officer of IBM, you know him well. And he talks about the journey that he's undertaken to transform the company into a data company. I think a lot of people don't really realize what's actually going on behind the scenes, whether it's financially oriented or revenue opportunities. But one of the things he stressed to me in our interview was that they're on average, they're reducing the end to end cycle time from raw data to insights by 70%, that's on average. And that's just an enormous, for a company that size, it's just enormous cost savings or revenue generating opportunity. >> There's no doubt that the technology behind data pipelines is improving and the process from moving data from those pipelines directly into predictive or diagnostic or prescriptive output is a lot more accelerated than the early days of data warehousing. >> Is the skills barrier is acute? It seems like it's lessened somewhat, the early Hadoop days you needed... Even data scientist... Is it still just a massive skill shortage, or we're starting to attack that. >> Well, I think companies are figuring out a way around the skill shortage by doing things like self service analytics and focusing on more easy to use mainstream type AI or advanced analytics technologies. But there's still very much a need for data scientists and organizations and the difficulty in finding people that are true data scientists. There's no real certification. And so really anybody can call themselves a data scientist but I think companies are getting good at interviewing and determining whether somebody's got the goods or not. But there are other types of skills that we don't really focus on, like the data engineering skills, there's still a huge need for data engineering. Data doesn't self-organize. There are some augmented analytics technologies that will automatically generate analytic output, but there really aren't technologies that automatically self-organize data. And so there's a huge need for data engineers. And then as we talked about, there's a large interest in external data and harvesting that and then ingesting it and even identifying what external data is out there. So one of the emerging roles that we're seeing, if not the sexiest role of the 21st century is the role of the Data Curator, somebody who acts as a librarian, identifying external data assets that are potentially valuable, testing them, evaluating them, negotiating and then figuring out how to ingest that data. So I think that's a really important role for an organization to have. Most companies have an entire department that procures office supplies, but they don't have anybody who's procuring data supplies. And when you think about which is more valuable to an organization? How do you not have somebody who's dedicated to identifying the world of external data assets that are out there? There are 10 million data sets published by government, organizations and NGOs. There are thousands and thousands of data brokers aggregating and sharing data. There's a web content that can be harvested, there's data from your partners and suppliers, there's data from social media. So to not have somebody who's on top of all that it demonstrates gross negligence by the organization. >> That is such an enlightening point, Doug. My last question is, I wonder how... If you can share with us how the pandemic has effected your business personally. As a consultant, you're on the road a lot, obviously not on the road so much, you're doing a lot of chalk talks, et cetera. How have you managed through this and how have you been able to maintain your efficacy with your clients? >> Most of our clients, given that they're in the digital world a bit already, made the switch pretty quick. Some of them took a month or two, some things went on hold but we're still seeing the same level of enthusiasm for data and doing things with data. In fact some companies have taken our (mumbles) that data to be their best defense in a crisis like this. It's affected our business and it's enabled us to do much more international work more easily than we used to. And I probably spend a lot less time on planes. So it gives me more time for writing and speaking and actually doing consulting. So that's been nice as well. >> Yeah, there's that bonus. Obviously theCUBE yes, we're not doing physical events anymore, but hey, we've got two studios operating. And Doug Laney, really appreciate you coming on. (Dough mumbles) Always a great guest and sharing your insights and have a great MIT CDOIQ. >> Thanks, you too, Dave, take care. (mumbles) >> Thanks Doug. All right. And thank you everybody for watching. This is Dave Vellante for theCUBE, our continuous coverage of the MIT Chief Data Officer conference, MIT CDOIQ, will be right back, right after this short break. (bright music)

>> Narrator: Live from Stanford University, it's theCUBE! Covering Stanford Women in Data Science 2020, brought to you by SiliconAngle Media. >> Hi, and welcome to the cube, I'm your host, Sonia Tagare. And we're live at Stanford University, covering the fifth annual WiDS, Women in Data Science Conference. Joining us today is Ya XU, the head of data science at LinkedIn. Ya Welcome to the cube. >> Thank you for having me. >> So tell us a little bit about your role and about LinkedIn. >> So LinkedIn is, first of all, the biggest professional social network, where we have a massive economic graph that we have been creating with millions actually close to 700 million members and millions of companies and jobs and of course, you know, with students of skills and also schools as well as part of it. And, and I lead the data science team at LinkedIn. And my team really spans across the global presence that LinkedIn offices have. And yeah really working on various different areas. That's both thinking about how we can iterate and understand and improve our products, that we deliver to our members and our customers. And also at the same time thinking about how we can make our infrast6ructure more efficient, and thinking about how we can make our sales and marketing more efficient as well, so we really span across. >> And how has the use of data science evolved to deliver a better user experience for users of LinkedIn? >> Yeah, so first of all, I think we LinkedIn in general, we truly believe that everybody can benefit from better data, better data access, in general. So we're certainly using data to continuously understand better of what our members are looking for. As a simple example, is that whenever we launch new feature, we're not just  blindly deciding ourselves what is the better feature for our members, but we actually understand how our users are reacting to it. Right? So we use data to understand that, and then certainly making decisions, and whether we should be eventually launching this feature to all members or not. So that's a very prominent way for us to use data. And obviously, we also use data to understand and just even before we build certain features. Is this sort of feature that's right feature to build. We do both survey and understand the survey data, but also at the same time understanding just user behavior data for us to be able to come up with better features for users. >> And do you use AB testing as well? >> Oh absolutely, Yeah. So we do a lot of AV experiments. That's what, I was not trying to use that word by that like that terminology, but this is what we use to have an understanding of user features that we are developing, that we are putting in front of our users. Is that what they enjoy as much as we think they will enjoy? >> Right, so you had a talk today about creating global economic opportunities with responsible data. So give us some highlights from your talk. >> So, first of all, at LinkedIn we we truly believe in the vision that we are working towards, which is really creating economic opportunity for every member of the global workforce. And if you're kind of starting from that, and thinking about that is our sort of the axiom that we're working towards, and then thinking about how you can do that, and obviously, the sort of the table stake or just the fundamental thing that we have to start with is to be able to preserve the privacy of our members as we are leveraging the data that our members entrust with us. Right, so how can we do that? We have some early effort in using and developing differential privacy as a technique for us to do a lot better. Always regarding preserving their privacy as we're leveraging the data, but also at the same time, it doesn't ends there, right? Because you're thinking about creating opportunity. It's not just about to preserve their privacy, but also, when we are leveraging the data, how can we leverage the data in a way that is able to create opportunity in a fair way? So here is also a lot of effort that we're having with regarding, how can we do that? And what does fairest mean? What are the ways we can actually turn some of the key concepts that we have into action that is really able to drive the way we develop product, the way that we think about responsible design, and the way that we build our algorithms, the way that we measure in every single dimension. >> And and speaking about that bias, at the opening address, they mentioned that diversity is really great because it provides many perspectives, and also helps reduce this bias. So how have you at LinkedIn been able to create a more diverse team? >> So first of all, I think it's certain we all believe that diversity is certainly better as we building product. Thinking about if you have a diverse team that is really a representation of the customer and some members that you're serving, then definitely you're able to come up with better features that is able to serve the needs of the population of our members. But also at the same time, that's just the right thing to do as well. Right, thinking about we all have had experiences we may not you know, feel as much belonging when we walk into a room that we are the only person that we identify with to be in that room. And, we certainly wanted to be able to create that environment for all the employees as well. And and thinking about, I think there is also studies that has done as what makes a high performing team. Some of the studies has done I google with the psychological safety aspects of it, which is really there's a lot of brain science that says when you make people feel they belong, that they will actually be so much more creative and innovative and everything right. So we have that belief. But tactically, there are many things that we're doing from all the divs aspect, right? How can you bring diversity, inclusion and belonging? Starting from and hiring, right? So we certainly are very much emphasized how can we increase the diversity of individuals that we're bringing to LinkedIn? And when they are at LinkedIn, can we make them feel more belonging, and feel more included in every aspects? We have different inclusion groups, right? We have I mean, obviously, I'm very much involved in Women tech. At LinkedIn we have both money efforts that we do to help women at LinkedIn in engineering, and in other groups as well to feel they belong to this community. At the same time, there is concrete actions that we're taking too. Right, that we are helping women to have a much better understanding, and aware of some of the ways that we operate that is slightly different from maybe our male colleagues will operate, right? There are certain things that we're doing to change the current processes, hiring processes, promotion process, that we are able to bring more equal footing to the way that we're thinking about gender gap and gender diversity. >> Right, that's great. And what advice would you give to women who are just starting college or who are just out of college who are interested in going into data science. >> So I want to say the biggest learning for me, is just have that can do attitude. I, you know, the woman biologically and all just like in every way, we're not any less than men. And that you certainly have seen many strong and very talented women that we have in the field. So don't let people's perceptions or biases around you to bring you down. And then thinking about what you wanted, and then just go for it, and then go for the the advice that you can get from people. And then there are so many as you can see in the conference today, so many talented women that you can reach out to who are winning and very willing to help you as well. >> And in this age of AI and ML, where do you see data science going in the future? >> That's a really interesting question. So in the way that, you know, data science I want to say is a field that is really broad, right? So if you're thinking about things that I would consider to be part of data science may not necessarily part of AI, but some of the course of influence that is extremely popular and important. And then I think the fields will continue to evolve, there are going to be and then the fields are continually overlapping with each other as well. You cannot do data science without understanding or have a strong skill in AI and machine learning. And you also can't do great machine learning without understanding the data science either. Right? So thinking about some of the talk that definitely colder earlier was sharing, as in you know, you can blind in the wrong algorithm and without realizing the bias. That all the algorithm is really just detecting the machines that's using the images versus you know, actually detecting the difference between broken bones or not right, like so. So I think having, I do see there is a continuously big overlap and I think the individuals who are involved in both communities should continue to be very comfortable being in that way too. >> Right, great. Thank you so much for being on theCUBE and thank you for your insight. >> Of course, thank you for having me. >> I'm your host, Sonia Takari. Thank you for watching theCUBE and stay tuned for more. (Upbeat music)

>> Announcer: Live from Stanford University. It's theCUBE, covering Stanford Women in Data Science 2020, brought to you by SiliconANGLE Media. (upbeat music) >> Hi, and welcome to theCUBE. I'm your host, Sonia Tagare. And we're live at Stanford University covering the fifth annual WiDS Women in Data Science Conference. Joining us today is Lucy Bernholz, who is the Senior Research Scholar at Stanford University. Lucy, welcome to theCUBE. >> Thanks for having me. >> So you've led the Digital Civil Society Lab at Stanford for the past 11 years. So tell us more about that. >> Sure, so the Digital Civil Society Lab actually exists because we don't think digital civil society exists. So let me take that apart for you. Civil society is that weird third space outside of markets and outside of government. So it's where we associate together, it's where we as people get together and do things that help other people could be the nonprofit sector, it might be political action, it might be the eight of us just getting together and cleaning up a park or protesting something we don't like. So that's civil society. But what's happened over the last 30 years really is that everything we use to do that work has become dependent on digital systems and those digital systems, some tier, I'm talking gadgets, from our phones, to the infrastructure over which data is exchanged. That entire digital system is built by companies and surveilled by governments. So where do we as people get to go digitally? Where we could have a private conversation to say, "Hey, let's go meet downtown and protest x and y, or let's get together and create an alternative educational opportunity 'cause we feel our kids are being overlooked, whatever." All of that information that get exchanged, all of that associating that we might do in the digital world, it's all being watched. It's all being captured (laughs). And that's a problem because both history and political science, history and democracy theory show us that when there's no space for people to get together voluntarily, take collective action, and do that kind of thinking and planning and communicating it just between the people they want involved in that when that space no longer exists, democracies fall. So the lab exists to try to recreate that space. And in order to do that, we have to first of all recognize that it's being closed in. Secondly, we have to make real technological process, we need a whole set of different kind of different digital devices and norms. We need different kinds of organizations, and we need different laws. So that's what the lab does. >> And how does ethics play into that. >> It's all about ethics. And it's a word I try to avoid actually, because especially in the tech industry, I'll be completely blunt here. It's an empty term. It means nothing the companies are using it to avoid being regulated. People are trying to talk about ethics, but they don't want to talk about values. But you can't do that. Ethics is a code of practice built on a set of articulated values. And if you don't want to talk about values, you don't really having conversation about ethics, you're not having a conversation about the choices you're going to make in a difficult situation. You're not having a conversation over whether one life is worth 5000 lives or everybody's lives are equal. Or if you should shift the playing field to account for the millennia of systemic and structural biases that have been built into our system. There's no conversation about ethics, if you're not talking about that thing and those things. As long as we're just talking about ethics, we're not talking about anything. >> And you were actually on the ethics panel just now. So tell us a little bit about what you guys talked about and what were some highlights. >> So I think one of the key things about the ethics panel here at WiDS this morning was that first of all started the day, which is a good sign. It shouldn't be a separate topic of discussion. We need this conversation about values about what we're trying to build for, who we're trying to protect, how we're trying to recognize individual human agency that has to be built in throughout data science. So it's a good start to have a panel about it, the beginning of the conference, but I'm hopeful that the rest of the conversation will not leave it behind. We talked about the fact that just as civil society is now dependent on these digital systems that it doesn't control. Data scientists are building data sets and algorithmic forms of analysis, that are both of those two things are just coated sets of values. And if you try to have a conversation about that, at just the math level, you're going to miss the social level, you're going to miss the fact that that's humanity you're talking about. So it needs to really be integrated throughout the process. Talking about the values of what you're manipulating, and the values of the world that you're releasing these tools into. >> And what are some key issues today regarding ethics and data science? And what are some solutions? >> So I mean, this is the Women and Data Science Conference that happens because five years ago or whenever it was, the organizers realize, "Hey, women are really underrepresented in data science and maybe we should do something about that." That's true across the board. It's great to see hundreds of women here and around the world participating in the live stream, right? But as women, we need to make sure that as you're thinking about, again, the data and the algorithm, the data and the analysis that we're thinking about all of the people, all of the different kinds of people, all of the different kinds of languages, all of the different abilities, all of the different races, languages, ages, you name it that are represented in that data set and understand those people in context. In your data set, they may look like they're just two different points of data. But in the world writ large, we know perfectly well that women of color face a different environment than white men, right? They don't work, walk through the world in the same way. And it's ridiculous to assume that your shopping algorithm isn't going to affect that difference that they experience to the real world that isn't going to affect that in some way. It's fantasy, to imagine that is not going to work that way. So we need different kinds of people involved in creating the algorithms, different kinds of people in power in the companies who can say we shouldn't build that, we shouldn't use it. We need a different set of teaching mechanisms where people are actually trained to consider from the beginning, what's the intended positive, what's the intended negative, and what is some likely negatives, and then decide how far they go down that path? >> Right and we actually had on Dr. Rumman Chowdhury, from Accenture. And she's really big in data ethics. And she brought up the idea that just because we can doesn't mean that we should. So can you elaborate more on that? >> Yeah well, just because we can analyze massive datasets and possibly make some kind of mathematical model that based on a set of value statements might say, this person is more likely to get this disease or this person is more likely to excel in school in this dynamic or this person's more likely to commit a crime. Those are human experiences. And while analyzing large data sets, that in the best scenario might actually take into account the societal creation that those actual people are living in. Trying to extract that kind of analysis from that social setting, first of all is absurd. Second of all, it's going to accelerate the existing systemic problems. So you've got to use that kind of calculation over just because we could maybe do some things faster or with larger numbers, are the externalities that are going to be caused by doing it that way, the actual harm to living human beings? Or should those just be ignored, just so you can meet your shipping deadline? Because if we expanded our time horizon a little bit, if you expand your time horizon and look at some of the big companies out there now, they're now facing those externalities, and they're doing everything they possibly can to pretend that they didn't create them. And that loop needs to be shortened, so that you can actually sit down at some way through the process before you release some of these things and say, in the short term, it might look like we'd make x profit, but spread out that time horizon I don't know two x. And you face an election and the world's largest, longest lasting, stable democracy that people are losing faith in. Set up the right price to pay for a single company to meet its quarterly profit goals? I don't think so. So we need to reconnect those externalities back to the processes and the organizations that are causing those larger problems. >> Because essentially, having externalities just means that your data is biased. >> Data are biased, data about people are biased because people collect the data. There's this idea that there's some magic debias data set is science fiction. It doesn't exist. It certainly doesn't exist for more than two purposes, right? If we could, and I don't think we can debias a data set to then create an algorithm to do A, that same data set is not going to be debiased for creating algorithm B. Humans are biased. Let's get past this idea that we can strip that bias out of human created tools. What we're doing is we're embedding them in systems that accelerate them and expand them, they make them worse (laughs) right? They make them worse. So I'd spend a whole lot of time figuring out how to improve the systems and structures that we've already encoded with those biases. And using that then to try to inform the data science we're going about, in my opinion, we're going about this backwards. We're building the biases into the data science, and then exporting those tools into bias systems. And guess what problems are getting worse. That so let's stop doing that (laughs). >> Thank you so much for your insight Lucy. Thank you for being on theCUBE. >> Oh, thanks for having me. >> I'm Sonia Tagare, thanks for watching theCUBE. Stay tuned for more. (upbeat music)

>> Announcer: Live from San Francisco, it's theCUBE covering RSA Conference 2020 San Francisco, brought to you by SiliconANGLE Media. >> Okay, welcome back, everyone, to CUBE's coverage here in San Francisco at RSA Conference 2020. I'm John Furrier, your host. You know, cybersecurity industry's changing. Enterprises are now awake to the fact that it's now a bigger picture around securing the enterprise, 'cause it's not only the data center. It's cloud, it's the edge, a lot of great stuff. We've got a great guest here from Dell EMC. Peter Gerr's a consultant, cyber resilience solutions and services marketing at Dell EMC. Great to see you. >> You too, John. >> Thanks for coming on. >> Good to see you again, thank you. >> So, you know, I was joking with Dave Volante just this morning around the three waves of cloud, public cloud, hybrid cloud, multicloud. And we see obviously the progression. Hybrid cloud is where everyone spends most of their time. That's from ground to cloud, on-premises to cloud. So pretty much everyone knows-- >> Peter: On-ramp, kind of. >> That on-prem is not going away. Validated by all the big cloud players. but you got to nail the equation down for on-premises to the cloud, whether it's, I'm Amazon-Amazon, Azure-Azure, whatever, all those clouds. But the multicloud will be a next generation wave. That as an industry backdrop is very, very key. Plus AI and data are huge inputs into solving a lot of what is going to be new gaps, blind spots, whatever insecurity. So I got to, you know, Dell has a history with huge client base, traditional enterprises transforming. You're in the middle of all this, so you got the airplane at 30,000 feet and the companies have to swap out their engines and reboot their teams, and it's a huge task. What's going on with cyber and the enterprises? What are some of the key things? >> Well, so I like to keep it pretty simple. I've been in this industry over 20 years and I've really consistently talked about data as the global currency, right? So it's beautifully simple. Whatever industry you're in, whatever size company you're in, enterprise or even now small to medium businesses, their businesses are driven by data. Connectivity to that data, availability of the data, integrity of the data, and confidentiality of the data. And so sort of the area of the world that I focus upon is protecting customers' most valuable data assets, now, whether those are on-prem, in the cloud, or in a variety of modalities, and ensuring that those assets are protected and isolated from the attack surface, and then ability to recover those critical assets quickly so they can resume business operations. That's really the area that I work in. Now, that data, as you pointed out, it could start on-prem. It could live in multicloud. It can live in a hybrid environment. The key is really to understand that not all data is created equally. If you were to have a widespread cyber attack, really the key is to bring up those critical applications systems and data sets first to return to business operations. >> Yeah, it's funny-- >> Peter: It's really challenging >> You know, it's not funny, it's actually just ironic, but it's really kind of indicative of the society now is that EMC was bought by Dell Storage and the idea of disruption has always been a storage concept. We don't want a lot of disruption when we're doing things, right? >> Peter: None, we can't, yeah. >> So whether it's backup and recovery or cyber ransomware, whatever it is, the idea of non-disruptive operations-- >> Absolutely. >> Has been a core tenant. Now, that's obviously the same for cyber, as you can tell. So I got to ask you, what is your definition and view of cyber resilience? Because, well, that's what we're talking about here, cyber resilience. What's your view on that? >> So when we started developing our cyber recovery solution about five years ago, we used the NIST cybersecurity framework, which is a very well-known standard that defines really five pillars of how organizations can think about building a cyber resilience strategy. A cyber resilience strategy really encompasses everything from perimeter threat detection and response all the way through incident response after an attack and everything that happens in between, protecting the data and recovering the data, right? And critical systems. So I think of cyber resilience as that holistic strategy of protecting an organization and its data from a cyber attack. >> That's great insight. I want to get your thoughts on how that translates into the ecosystem, because this is an ecosystem around cyber resilience. >> Peter: Absolutely. >> And let's just say, and you may or may not be able to comment on this, but RSA is now being sold. >> Peter: Yeah, no, that's fair. >> So that's going out of the Dell family. But you guys have obviously VMware and Secureworks. But it's not just you guys. It's an ecosystem. >> It really is. >> How does Dell now without, with and without RSA, fit into the ecosystem? >> So as I mentioned, cyber resilience is really thought of as a holistic strategy. RSA and other Dell assets like Carbon Black fit in somewhere in that continuum, right? So RSA is really more on threat detection and response, perimeter protection. The area of the business that I work on, data protection and cyber recovery, really doesn't address the prevention of attacks. We really start with the premise that preventing a cyber attack is not 100% possible. If you believe that, then you need to look at protecting and recovering your assets, right? And so whether it's RSA, whether it's Carbon Black, whether it's Secureworks, which is about cyber incident and response, we really work across those groups. It's about technology, processes, and people. It's not any one thing. We also work outside of the Dell technologies umbrella. So we integrate, our cyber recovery solution is integrated with Unisys Stealth. So there's an example of how we're expanding and extending the cyber recovery solution to bring in other industry standards. >> You know, it's interesting. I talk to a lot of people, like, I'm on theCube here at RSA. Everyone wants better technology, but there's also a shift back to best-of-breed, 'cause you want to have the best new technology, but at the same time, you got to have proven solutions. >> Peter: That's the key. >> So what are you guys selling, what is the best-of-breed from Dell that you guys are delivering to customers? What are some of the areas? >> So I'm old EMC guy myself, right? And back from the days of disaster recovery and business continuity, right? More traditional data protection and backup. The reality is that the modern threats of cyber hackers, breaches, insider attacks, whatever you like, those traditional data protection strategies weren't built to address those types of threats. So along with transformation and modernization, we need to modernize our data protection. That's what cyber recovery is. It's a modern solution to the modern threat. And what it does is it augments your data, excuse me, your disaster recovery and your backup environment with a purpose-built isolated air gap digital vault which is built around our proven Data Domain and PowerProtect DD platforms that have been around for over a decade. But what we've done is added intelligence, analytics, we've hardened that system, and we isolate it so customers can protect really their most valuable assets in that kind of a vault. >> So one of things I've been doing some research on and digging into is cyber resilience, which you just talked about, cyber security, which is the industry trend, and you're getting at cyber recovery, okay? >> Peter: Correct. >> Can you talk about some examples of how this all threads together? What are some real recent wins or examples? >> Sure, sure. So think of cyber recovery as a purpose-built digital vault to secure your most valuable assets. Let me give you an example. One of our customers is a global paint manufacturer, okay? And when we worked with them to try to decide what of their apps and data sets should go into this cyber recovery vault, we said, "What is the most critical intellectual property "that you have?" So in their case, and, you know, some customers might say my Oracle financials or my Office 365 environment. For this customer it was their proprietary paint matching system. So they generate $80 to $100 million every day based upon this proprietary paint matching system which they've developed and which they use every day to run their business. If that application, if those algorithms were destroyed, contaminated, or posted on the public internet somewhere, that would fundamentally change that company. So that's really what we're talking about. We're working with customers to help them identify their most critical assets, data, systems, applications, and isolate those from the threat vector. >> Obviously all verticals are impacted by cyber security. >> Every vertical is data-driven, that's right. >> And so obviously the low-hanging fruit, are they the normal suspects, financial services? Is there a particular one that's hotter than, obviously financial services has got fraud and all that stuff on it, but is that still number one, or-- >> So I think there's two sides to the coin. One, if you look at the traditional enterprise environments, absolutely financial services and healthcare 'cause they're both heavily regulated, therefore that data has very high value and is a very attractive target to the would-be hackers. If you look on the other end of the spectrum, though, the small to medium businesses that all rely on the internet for their business to run, they're the ones that are most susceptible because they don't have the budgets, the infrastructure, or the expertise to protect themselves from a sophisticated hacker. So we work across all verticals. Obviously the government is also very susceptible to cyber threats. But it's every industry, any business that's data-driven. I mean, everyone's been breached so many times, no one even knows how many times. I got to ask you about some cool trends we're reporting on here. Homomorphic encryption is getting a lot of traction here because financial services and healthcare are two-- >> Peter: Homomorphic? >> Homomorphic, yeah. Did I say that right? >> It's the first time I've ever heard that term, John. >> It's encryption at in use. So you have data at rest, data in flight, and data in use. So it's encryption when you're doing all your, protecting all your transactional data. So it's full implementation with Discovery. Intel's promoting it. We discovered a startup that's doing that, as well. >> Peter: Yeah, that's new for me, yeah. >> But it allows for more use cases. But data in use, not just motion, or in-flight, whatever they call it. >> Peter: I get it, yeah, static. >> So that's opening up these other thing. But it brings up the why, why that's important, and the reason is that financial services and healthcare, because they're regulated, have systems that were built many moons ago or generations ago. >> Absolutely. >> So there was none of these problems that you were mentioning earlier, like, they weren't built for that. >> Correct. >> But now you need more data. AI needs sharing of data. Sharing is a huge deal. >> Real-time sharing, too, right? >> Real-time sharing. >> And I think that's where the homomorphic encryption comes in. >> That's exactly right. So you mentioned that. So these industries, how can they maintain their existing operations and then get more data sharing? Do you have any insight into how you see that? Because that's one of those areas that's becoming like, okay, HIPAA, we know why that was built, but it's also restrictive. How do you maintain the purity of a process-- >> If your infrastructure is old? That is a challenge, healthcare especially, because, I mean, if I'm running a health system, every dollar that I have should really go into improving patient care, not necessarily into my IT infrastructure. But the more that every industry moves towards a real-time data-driven model for how we give care, right, the more that companies need to realize that data drives their business. They need to do everything they can to protect it and also ensure that they can recover it when and if a cyber attack happens. >> Well, I really appreciate the insight, and it's going to be great to see Dell Technologies World coming up. We'll dig into a lot of that stuff. While we're here and talking us about some of these financial services, banking, I want to get your thoughts. I've been hearing this term Sheltered Harbor being kicked around. What is that about? What does that mean? >> Sheltered Harbor, you're right, I think you'll hear a lot more about it. So Sheltered Harbor is a financial industries group and it's also a set of best practices and specifications. And really, the purpose of Sheltered Harbor is to protect consumer and financial institutions' data and public confidence in the US financial system. So the use case is this. You can imagine that a bank having a cyber attack and being unable to produce transactions could cause problems for customers of that bank. But just like we were talking about, the interconnectedness of the banking system means that one financial institution failing because of a cyber attack, it could trigger a cascade and a panic and a run on the US financial banks and therefore the global financial system. Sheltered Harbor was developed to really protect public confidence in the financial system by ensuring that banks, brokerages, credit unions are protecting their customer data, their account records, their most valuable assets from cyber attack, and that they can recover them and resume banking operations quickly. >> So this is an industry group? >> It's an industry group. >> Or is it a Dell group or-- >> No, Sheltered Harbor is a US financial industry group. It's a non-profit. You can learn more about it at shelteredharbor.org. The interesting thing for Dell Technologies is we're actually the first member of the Sheltered Harbor solution provider program, and we'll be announcing that shortly, in fact, this week, and we'll have a cyber recovery for Sheltered Harbor solution in the market very shortly. >> Cyber resilience, great topic, and you know, it just goes to show storage is never going away. The basic concepts of IT, recovery, continuous operations, non-disruptive operations. Cloud scale changes the game. >> Peter: It's all about the data. >> It's all about the data. >> Still, yes, sir. >> Thanks for coming on and sharing your insights. >> Thank you, John. >> RSA coverage here, CUBE, day two of three days of coverage. I'm John Furrier here on the ground floor in Moscone in San Francisco. Thanks for watching (electronic music)

>> Announcer: Live from San Francisco, it's theCUBE covering RSA Conference 2020, San Francisco. Brought to you by SiliconANGLE Media. >> Welcome back everybody, Jeff Frick with theCUBE. We're at RSA 2020. It's day four, it's Thursday. This is a crazy long conference, 40,000 people. Even with the challenges presented by coronavirus, and there's a lot of weird stuff going on, the team pulled it together, they went forward. And even though there was drops out here and there, I think all in all, most people will tell you, it's been a pretty successful conference. And we're excited to be joined by really one of the top level sponsors here, that's still here and still doing good things. It's Vittorio Viare... Viarengo, sorry, the new interim CMO of McAfee. >> Yeah. >> Vittorio, I just call you Vittorio all the time. I never look past your first name. Great to see you. >> Likewise. It's always a pleasure to be here with an institution of Silicon Valley-- >> Oh thank you, thank you. So interim CMO, I always think of like interim football coaches that they get pulled in halfway through the season, so the good news is you kind of got the job and all the responsibilities. The bad news is, you still have that interim thing, but you don't care, you just go to work, right? >> Now whenever you have an interim job, you have to just do the job and then that's the best way to operate. >> Yeah, so again, I couldn't help but go back and look at that conversation that we had at Xerox Parc, which is interesting. That's pretty foundational, everything that happens in Silicon Valley, and so many discoveries up there. And you touched on some really key themes in the way you manage your teams, but I think they're really much more valuable, and worth bringing back up again. And the context was using scrum as a way to manage people, but more importantly, what you said is it forced you as a leader to set first priorities and have great communication; and to continually do that on this two week pace, to keep everybody moving down the road. I think that is so powerful and so lacking unfortunately, in a lot of organizations today. >> Yeah, look, I think that when you hire smart people, if you just make sure that they understand what their priorities are, and then remove the obstacle and get out of the way, magical things happen. And I give you example that is very close to your heart. When I took over a great team at Skyhigh, that got bought by McAfee, they had content marketing down to a science, but they were lacking videos. So I brought that in. I said, "Guys, people watch videos, "people engage with videos, "we need to start telling the story through videos." And I started pushing, pushing, pushing, and then I pulled back, and these guys took it to a whole new level. And then they're doing videos, they're very creative, they are crisp. And I'm like, "Yeah, my job is done." >> It is really wild how video has become such an important way for education. I mean it used to be... I remember the first time I ever saw an engineer use Google to answer a question on writing code. I had never seen that before. I'm not a coder. Wow, I thought it was just for finding my local store or whatever. And now to see what really... I think YouTube has pushed people to expect that the answer to any question should be in a video. >> So, yesterday literally, somebody from a company I don't even know stopped me and said, "I watch you to videos on container. "Thank you very much." I was like, "What, you?" And the genesis of that was the sales people ask me, "Hey, we're selling container security and all that," but I don't even understand what containers are. Okay, sure. So I shot a video and I'm the CMO, I was the vice president. I think you have to put your face on your content. It doesn't matter how senior you are, you're not in a corner office, you're down there with the team. So I got into the studio, based on my background at VMware, I knew virtual machine, and I said, "Okay, how do you explain this "to somebody who's not technical?" And next thing you know, it makes its way out there, not just to our sales force, but to the market at large. That's fantastic. >> Right, and let me ask you to follow up on that because it seems like the world is very divergent as to those who kind of want their face, and more their personality to be part of their business culture and their business messaging, and those that don't. And you know, as part of our process, we always are looking at people's LinkedIn, and looking at people's Twitter. I get when people don't have Twitter, but it really surprises me when professionals, senior professionals within the industry aren't on LinkedIn. And is just like, wow! That is such a different kind of world. >> LinkedIn right now is... and I'm stealing this from Gary on the Chuck, as a big believer in this. LinkedIn right now is like Facebook 10 years ago. You get amazing organic distribution, and it's a crime not to use it. And the other thing is if you don't use it, how are you going to inspire your team to do the right thing? Modern marketing is all about organic distribution with a great content. If you're not doing it yourself... I grew up in a bakery. I used to look at my mom, we have a big bakery. We had eight people working, and I said, "Ma, why are you workin' so hard? "Your first day, last hour?" And she said, "Look, you cannot ask your people, "to work harder than you do." That was an amazing lesson. So it's not just about working hard, and harder than your team, it's about are you walking the walk? Are you doing the content? Are you doing the modern marketing things that work today, if you expect your people to also do it? >> Yeah, it's just funny 'cause, when we talk to them, I'm like, "If you don't even have a LinkedIn account, "we shouldn't even be talking to you "because you just won't get what we do. "You won't see the value, you won't understand it "and if you're not engaging at least "a little bit in the world then..." And then you look at people say like Michael Dell, I'll pick on or Pat Gelsinger who use social media, and put their personalities out there. And I think it's, people want to know who these people are, they want to do business with people that they they like, right? >> Absolutely. You know what's the worst to me? I can tell when an executive as somebody else manages their account, I can tell from a mile away. That's the other thing. You have to be genuine. You have to be who you are on your social and all your communication because people resonate with that, right? >> Right. All right, so what are you doing now? You got your new title, you've got some new power, you've got a great brand, leading brand in the industry, been around for a while, what are some of your new priorities? What's some of the energy that you're bringing in and where you want to to go with this thing? >> Well, my biggest priority right now is to get the brand and our marketing to catch up with what the products and the customers are already which is, Cloud, Cloud, Cloud. So when we spun off from Intel two years ago, we had this amazing heritage in the endpoint security. And then we bought Skyhigh, and Skyhigh was transformational for us because it became the foundation for us to move to become a cloud-first organization. And is in the process of becoming a cloud-first organization, and creating a business that is growing really fast. We also brought along the endpoint, which now is all delivered from the Cloud, to the cloud-first open unified approach, which is exciting. >> And we see Edge is just an extension of endpoints, I would assume. It just changes the game. >> Yeah, so if you think about today modern work gets done with the backend in the Cloud, and accessing those backends from the device, right? >> Right. >> And so, our strategy is to secure data where modern work gets done, and it's in the device, in the Cloud, and on the edge. Because data moves in and out of the Cloud, and that's kind of the edge of the Cloud. That's what we launched this week at RSA we launched Unified Cloud Edge, which is our kind of a, Gartner call's it SaaS-y, so that we are kind of the security. We believe we have the most complete and unified security part of the SaaS-y world. >> Okay, I just laugh at Gartner and the trough of disillusion men and Jeff and I always go back to a Mars law. Mar does not get enough credit for a Mars law. We've got a lot of laws, but Mars law, we tend to overestimate in the short term, the impact of these technologies, and they completely underestimate really the long tail of this technology improvements, and we see it here. So let's shift gears a little bit. When you have your customers coming in here, and they walk into RSA for the first time, how do you tell people to navigate this crazy show and the 5,000 vendors and the more kind of solutions and spin vocabulary, then is probably save for anyone to consume over three days? >> Look, security is tough because you look around and say, "You have six, 700 vendors here." It's hard to stand out from the crowd. So what I tell our customers is use this as a way to meet with your strategic vendors in the booth upstairs. That's where you conduct business and all that. And I walk around to see from the ground up, send your more junior team out there to see what's happening because some of these smaller companies that are out here will be the big transformational companies or the future like Skyhigh was three four years ago, and now we're part of McAfee, and leading the charge there. >> Yeah, just how do you find the diamond in the rough, right? >> Yeah. >> 'Cause there's just so much. But it's still the little guys that are often on the leading edge and the bleeding edge, of the innovation so you want to know what's going on so that you're kind of walking into the back corners of the floor as well. >> That's why I am lifelong learner, so I go around to see what people do from a marketing perspective because, the last thing I want to do, I want to become obsolete. (Jeff laughs) And the way you don't become obsolete is to see what the new kids on the block do and steal their ideas, steal their tactics take them to the next level. >> Right, so I want to ask you a sensitive question about the conference itself and the coronavirus thing and we all saw what happened in Mobile World Congress. I guess it just got announced today that Facebook pulled F8, their developer conference. We're in the conference business. You go to a lot of conferences. Did you have some thought process? There were some big sponsors that pulled out of this thing. How did you guys kind of approach the situation? >> It's a tough one. >> It's a really tough one. >> It's a very tough one 'cause last thing you want to do is to put your employees and your customers at risk. But the way we looked at it was there were zero cases of coronavirus in San Francisco. And we saw what the rest of the industry was doing, and we made the call to come here, give good advice to our employees, wash their hands, and usual and this too will pass. >> Yeah, yeah. Well Vittorio, it's always great to catch up with you. >> Likewise. >> I just loved the energy, and congratulations. I know you'll do good things, and I wouldn't be at all surprised if that interim title fades away like we see with most great coaches. >> Good. >> So thanks for stopping by. >> My pleasure. >> All right, he's Vittorio, I'm Jeff. You're watching theCUBE, we're at RSA 2020 in San Francisco. Thanks for watching, we'll see you next time. (upbeat music)

>> Narrator: Live from San Francisco. It's theCUBE. Covering RSA conference 2020 San Francisco. Brought to you by SiliconANGLE Media. (upbeat music) >> Hey, welcome back, everybody. Jeff Rick here with theCUBE. We're at RSA 2020, Moscone and beautiful San Francisco's day four I think Thursday already. This is a crazy conference Monday, Tuesday, Wednesday, Thursday, and Friday. I don't think we'll be here for tomorrow. It's been a pretty full slate. As it is, we're excited to have our next guest. She is Rose Ross, the founder and chief trailblazer, for Tech Trailblazers. Rose. Great to meet you. >> It's great to be here too. >> Absolutely. So what are the Tech Trailblazers? >> So the Tech Trailblazers are an awards lead platform, which recognizes the creme de la creme of the enterprise Tech startup landscape. >> Jeff: Okay. >> So we cover the categories from AI through to storage, but obviously security is a big part of that and we find that security and cloud are usually our most popular awards to be entered into. >> Okay, and I assume you're, really recognizing the individuals more than the companies, >> We do both. >> Or is it more of the companies? You do both. >> We do the Tech category so they can compare like for like apples with apples, pears with pears, security startups with security startups. And then we also acknowledge and recognize some of the key players in those startups. So we have a female trailblazers and a male trailblazer each year . >> Okay, and how long have you been doing this? >> This is our eighth edition. >> The eighth edition. >> Started for a while. 2012 was our first outing. >> Okay, And you said you just gave out this year's Awards on Monday? >> That's right. We announced it. Yeah, day one of RSA. >> Right, so give us some of the highlights. Who were some of the special people that you called out this year? >> Some of the special people, I actually sat down with one of the special people just now interviewed CEO of Shift-left who is our security trailblazer this year. Manish Gupta and yeah, we spent some time chatting about his journey and his challenges and his successes. And finding out more about the technology itself. So. >> And so what are the criteria to win? >> So we kind of look at a number of elements. We have an independent body of judges who are from the analyst community, from the blogger community from industry itself. So we have CSOs, CIOs, and just people who understand the Technology really, at both the technical level and what is needed by the marketplace. So we look at a number of things. One is obviously innovation. If you're looking at the startup world, you want to look at people who are bringing new and exciting things that are needed by companies, to either secure them or store their data or analyze their data. But we also look at how they're doing in the market. So, we'll be looking at what their go to market strategy is, how they're engaging with the end user community, that type of stuff. >> Okay. And at what stage in their growth are they generally you know, kind of coming into your radar? >> So we sort of do the cutoff for a start up as being having not celebrated their sixth birthday yet. >> Six birthday okay. >> Right, so and have not gone beyond Series C funding. >> Okay. >> So you wanted to keep it on the the newer end of the startup spectrum. We also have a special award for those that have not received any VC funding whatsoever. So they're either growing organically or privately funded. That could be seed capital, you know, crowdfunding, whatever that might be. And they have to be two years or younger, and they are all fire starters. >> And those are fire starters. So those are probably it's just really a function of life, 'cause I would imagine the vast majority of the companies that you recognize, eventually get VC funding if you're playing in this crazy technology space. >> It certainly helps to get to where you want to go. Accelerate, put a bit more fuel in the tank. >> So you also announced in your press release the incredible amount of money (laughs) your award winners have raised over time. Do you tell us a little bit more about that? >> Well, yeah, with RSA this week, we thought it'd be a great time to reflect back on what our security trailblazers had done over these eight editions. And obviously, it's a little bit early for expecting additional fundraising from Shift-left, 'cause they literally got the award on Monday. >> Great. >> But hopefully, if you look at the history of it all, we look at the people who've received the accolade over the last eight editions, nearly all of them have been within their first two years. Most of them have done at least one round of funding, but have usually gone on to do another significant round of funding within 12 months of having one, we'd love to take all the credit for that, but I think you really need to put that on the team. >> Jeff: Right. >> And acquisitions have also been quite prevalent. So we looked at the numbers just before RSA, and it was 72 722 million of the disclosed raised, and just in the security, >> Right. >> Space. Unfortunately, or very fortunately for one of our winners, ZeroFOX, they just peeped in with raising 74 million last Friday, which we didn't include. So if we put the undisclosed it would definitely over 800 million now. So well done to the ZeroFOX guys. >> Right, so how did you get involved in this? >> It was an idea that I had. My my other life is a Tech PR person. And we were working on a campaign for a show somewhat like RSA in the UK. And we thought it would be a great idea to run a startup competition to highlight some new entrants to the market. Unfortunately, they didn't think it was a fit for what they wanted to do, but it was such a compelling idea. I've worked with startups all my life and one of the challenges was always with them, particularly in the early stages to get recognition and to get coverage. So we thought we can do something about this. And I thought, well, nobody's going to listen to a PR person. They aren't interested in what I think. I'm not an expert on who's great in this space. So I spoke to Joe Bagley, who's the CTO of Amir for VMware, who's somebody I've worked with a lot over the years. And I said, Look, Joe, if I run something like this, would you come on board as a judge? And he said, Absolutely, I think it's a brilliant idea. And luckily, many other amazing judges has followed in his footsteps. So it's thanks to them, so. >> How many judges are there? >> We have around 40. I mean, we have a number of what a number of categories. So we want a specialist in those areas. Some cover multiple light cloud and security or Cloud and Storage. But obviously, when you look at AI and blockchain and all these other categories, you need people who really understand that space. >> And what's the process kind of how big is the top of the funnel when he started? And then how do you kind of whittle it down to the end when you said 1212 categories, so 12 winners per year about? >> Yeah. So we started off as obviously people enter usually through their PR team or their marketing team, or pull together the information that we request, which is quite a lengthy process, it's a big commitment of time. But not huge, but we do want to get to a certain amount of detail, to make a decision and give the judges something to work with. Then for that period, we then put out the judges to create the shortlist. So they will come back they will score on a number of elements, which are things like innovation and the maturity of the technology, then go to market attractiveness and their own personal view of how exciting and it is intuitive and how trailblazing it actually is. >> Right. >> Then we put it out to a public vote, but also the judges then take the shortlist and take another look at everybody. >> And it gets a public vote too? >> Yes, it does. >> It so does. Do the judges ever meet with the the nominees or is it all done based on the application the application packet that you put together and any other independent information they find on their own? >> Well, we still would encourage. I know the judges do like to reach out to people. And I know that obviously there are relationships because of the nature of the types of judges. >> Jeff: Sure. >> Obviously, we've got people in industry within the vendor community, analysts and bloggers, so they will have people that they know. So I always encourage people, if they say, you know, what would you do? I said, Well, if I was you, I would also reach out to the judges in your area, and just make them aware of who you are. And if they have other questions that they should you know, set up a briefing or something. >> Right. So it's really interesting concept to get the pub into the startup world because it's really, as you know, being in PR, you know, it's really hard to get elevated above the noise, if you will. And you know, we're sitting here surrounded by I don't even know how many thousands of vendors are in this hall. >> The early stage has 51 just as a starter. >> 51 in the early stage expo. >> Yes. >> Which hall is that? >> It's up on the second floor. >> On the second floor. Then there's little like corners of cubbies have of not even 10 by 10s. But you know the kind of the classic kiosks. So, when you're talking to two small companies, regardless of whether they go for the word, what do you tell them as a PR pro? What do you tell them as someone who's, you know, kind of seeing the challenges of trying to raise your profile as a small company? Do you stick to your knitting? Do you in a try to get a high profile? When you know, what are some of the tips and tricks that help little companies rise above the den, if you will, in this great space. >> Validation is always very important. Talk to the influencers in your space, talk to the analysts in your space, the bloggers in your space, and get that feedback and integrate it into your plan of how you create your message. And I think that's one of the hard things, a lot of startups particularly in the technology space, particularly enterprise Tech, they really in the weeds with what's amazing about their products and why they put it together. But you really have to put that into very simple terms. >> Jeff: Right. >> I mean, if you look at someone like RSA, we have got, you know, a lot of buzzwords kicking around here. You do have to try and put that into the deeds and requirements of the end user community. That's always got to be your lens on things >> Right. >> really. >> And you also you always have the vendor viability issues, you know, with your top and even if your Tech relatively inexpensive, maybe as a PLC or this or that, it still takes an investment from your potential customers to put it in and take that risk. And, you know, that's a much bigger hurdle to overcome often than simply the pricing or the structure of the deal. Not a easy, not an easy path. >> It has to be a partnership. I mean, one of the things we were advocating a couple years ago is that the bigger organizations really should have somebody who has a role of being a Chief Collaboration Officer for those smaller companies to engage with them. Because even the procurement process can obviously kill you. >> A little kill a little company, right? Even the pre sales, just having meetings and meetings and meetings and meetings and meetings and meetings to talk about the meetings that you're going to have to maybe eventually (laughs) get to somebody who can make a decision. >> Yeah, Its tough. >> Very cool. >> So, any kind of significant changes in the programme over time? Are you pretty much at the same place you were eight years ago? Or do you see this expanding into different categories? How do you see, you know, kind of the evolution of the Trailblazer? >> Well, we like to review everything and we listened to our judges, we listened to people in the marketplace. I mean, I had a great meeting yesterday with somebody in banking, who works with an awful lot of startups. And there is some really good news coming through that. The enterprise Tech VC community, there's a lot more of an appetite. They're starting to see the value more and more of investing in that type of longer longer term, because you can actually scale beyond where you can do sometimes with a consumer Technology. >> Right >> The potential unicorn sometimes don't quite make it. Those horses aren't always that reliable in the race. >> (laughs) Sometimes too much money is not a good thing that is for sure. >> Yeah. >> Or is good for you? It's a great way I know, I think the kind of the award format is a great way to shine a little bit of extra light on some of these companies that are really struggling to get noticed. It's a really difficult process for a startup, especially in such a deep Technology field. Something is so mission critical that people it's just not that easy for people to give you a try and give you a trial. Takes a lot of investment. So good work and look forward >> Thank you. to continuing to see the winners, raise lots of money and have success. >> Right, absolutely. Thank you, Jeff. >> All right Rose thanks again. She's Rose, I'm Jeff. You're watching theCUBE. We're at RSA 2020. Thanks for watching, we'll see you next time. (upbeat music)

>> Announcer: Live from San Francisco, it's theCUBE, covering RSA Conference 2020 San Francisco, brought to you by SiliconANGLE Media. >> Hey, welcome back everybody, Jeff Frick here with theCUBE. We are Thursday, day four of the RSA Show here in Moscone in San Francisco. It's a beautiful day outside, but the show is still going, 40,000-plus people. A couple of challenges with the coronavirus, and some other things going on, but everybody's here, everybody's staying the course, and I think it's really a good message going forward as to what's going to happen in the show season. We go to a lot of shows. Is 2020 the year we're going to know everything with the benefit of hindsight? It's not quite working out so far that way, but we're bringing in the experts to share the knowledge, and we're excited for our next guest, who's going to help us get to know what the answers are. He's Oliver Sherman, senior director, Enterprise Product Marketing for Juniper Networks. Oliver, great to see you. >> Thanks for having me. >> Absolutely, so first off, just general impressions of the show. I'm sure you've been coming here for a little while. >> We have, and I think the show's going very well, as you pointed out, there's a couple of challenges that are around, but I think everybody's staying strong, and pushing through, and really driving the agenda of security. >> So I've got some interesting quotes from you doing a little research for this segment. You said 2019 was the year of enforcement, but 2020 is the year of intelligence. What did you mean by that? >> Specifically, it's around Juniper. We have a Juniper connected security message and strategy that we proved last year by increasing the ability to enforce on all of your infrastructure without having to rip and replace technologies. For instance, on our widely rolled out MX routing platform, we offer second tell to block things like command and control traffic, or on our switching line for campus and data centers, we prevent lateral threat propagation with second tell, allowing you to block hosts as they're infected, and as we rounded that out, and it's a little bit in 2020 we were able to now deliver that on our Mist, or our wireless acquistion that we did last year around this time, so showing the integration of that product portfolio. >> Yeah, we met Bob Friday from Mist. >> Oliver: Excellent. >> He, doing the AI, some of the ethics around AI. >> Oliver: Sure. >> At your guys conference last year. It was pretty interesting conversation. Let's break down what you said a little bit deeper. So you're talking about inside your own product suite, and managing threats across once they get to that level to keep things clean across that first layer of defense. >> Right, well, I mean, whether you're a good packet or a bad packet, you have to traverse the network to be interesting. We've all put our phones in airplane mode at Black Hat or events like that because we don't want anybody on it, but they're really boring when they're offline, but they're also really boring to attackers when they're offline. As soon as you turn them on, you have a problem, or could have a problem, but as things traverse the network, what better place to see who and what's on your network than on the gears, and at the end of the day, we're able to provide that visibility, we're able to provide that enforcement, so as you mentioned, 2020 is now the year of an awareness for us, so the Threat Aware Network. We're able to do things like look at encrypted traffic, do heuristics and analysis to figure out should that even be on my network because as you bring it into a network, and you have to decrypt it, a, there's privacy concerns with that in these times, but also, it's computationally expensive to do that, so it becomes a challenge from both a financial perspective, as well as a compliance perspective, so we're helping solve that so you can offset that traffic, and be able to ensure your network's secure. >> So is that relatively new, and I apologize. I'm not deep into the weeds of feature functionality, but that sounds pretty interesting that you can actually start to do the analysis without encrypting the data, and get some meaningful, insightful information. >> Absolutely, we actually announced it on Monday at 4:45 a.m. Pacific, so it is new. >> Brand new. >> Yes. >> And what's the secret sauce to be able to do that because one would think just by rule encryption would eliminate the ability to really do the analysis, so what analysis can you still do while still keeping the data encrypted? >> You're absolutely right. We're seeing 70 to 80% of internet traffic is now encrypted. Furthermore, bad actors are using that to obfuscate themselves, right, obviously, and then, the magic to that, though, to look at it without having to crack open the package is using things like heuristics that look at connections per second, or connection patterns, or looking at significant exchanges, or even IP addresses to know this is not something you want to let in, and we're seeing a very high rate of success to block things like IoT botnets, for instance, so you'll be seeing more and more of that from us throughout the year, but this is the initial step that we're taking. >> Right, that's great because so much of it it sounds like, a, a lot of it's being generated by machines, but two, it sounds like the profile of the attacks keeps changing quite a bit from a concentrated attacks to more, it sounds like now, everyone's doing the slow creeper to try to get it under the covers. >> Right, and really, you're using your network to your full extent. I mean, a lot of things that we're doing including encrypted traffic analysis is an additional feature on our platform, so that comes with what you already have, so rather than walking in and saying, "Buy my suite of products, this will all" "solve all your problems," as we've done for the past, or as other vendors have done for the past 10, 20 years, and it's never worked. So you why not add things that you already have so you're allowed to amortize your assets, build your best of breed security, and do it within a multi-vendor environment, but also, do it with your infrastructure. >> Right, so I want to shift gears a little bit. Doing some research before you got on, you've always been technical lead. You've been doing technical lead roles. You had a whole bunch of them, and we don't have internet, unfortunately, here, so I can't read them off. >> Oliver: That's fine. >> But now, you've switched over. You've put the marketing hat on. I'm just curious the different, softer, squishy challenge of trying to take the talent that you have, the technical definitions that you have, the detailed compute and stuff you're doing around things like you just described, and now, putting the marketing hat, and trying to get that message out to the market, help people understand what you're trying to do, and break through, quite frankly, some crazy noise that we're sitting here surrounded by hundreds, if not thousands of vendors. >> I think that's really the key, and yes, I've been technical leads. I've run architecture teams. I've run development teams, and really, from a marketing perspective, it's to ensure that we're delivering a message that is, that the market will consume that is actually based in reality. I think a lot of times you see a lot of products that are put together with duct tape, baling twine, et cetera, but then, also have a great Powerpoint that makes it look good, but from a go to market perspective, from whether it's your sellers, meaning the sellers that work for Juniper, whether it's our partners, whether it's our customers, they have to believe in what's out there, and if it's tried and true, and we understand it from an engineering perspective, and we can say it's not a marketing texture, it's a strategy. >> Right. >> That really makes a difference, and we're really seeing that if you look at our year over year growth in security, if you look at what analysts are saying, if you look at what testing houses are saying about our product, that Juniper's back, and that's why I'm in this spot. >> And it really begs to have a deeper relationship with the customer, that you're not selling them a one-off market texture slide. You're not having a quick point solution that's suddenly put together, but really, have this trusted, ongoing relationship that's going to evolve over time. The products are going to evolve over time because the threats are evolving over time, right? >> Absolutely, and to help them get more out of what they already have, and from a go to market perspective, our partners have an addressful market that's naturally through the install base that we have, we're able to provide additional value and services to those customers that may want to lean on a partner to actually build some of these solutions for them. >> All right, well, Oliver, well thanks for stopping by. I'm glad I'm not too late on the encrypted analysis game, so just a couple of days. >> Absolutely. >> Thanks for stopping by. Best to you, and good luck with 2020, the year we'll know everything. >> Absolutely, thanks for having me. >> All right, he's Oliver, I'm Jeff, you're watching theCUBE. We're at RSA 2020 here in Moscone. Thanks for watching. We'll see you next time. (gentle electronic music)

>> Announcer: Live from San Francisco, it's theCUBE, covering RSA conference 2020 San Francisco, brought to you by SiliconANGLE Media. >> Hey, welcome back, everybody. Jeff Frick here with theCUBE. We're at the RSA 2020 show, here in Moscone in San Francisco, it's Thursday, we've been going wall to wall, we're really excited for our next guest. We've been talking about some kind of interesting topics, getting a little bit into the weeds, not on the technology, but some of the philosophical things that are happening in this industry that you should be thinking about. And we're excited welcome, Laurence Pitt, he is the cyber security strategist at Juniper Networks. Laurence, great to meet you. >> Thank you very much, hi. >> Yeah, so before we turn the cameras off, we've been talking about all kinds of fancy things, so let's just jump into it. One of the topics that gets a lot of news is deepfakes, and there's a lot of cute funny things out there of people's voices and things that they're saying not necessarily being where you expect them to be, but there's a real threat here, and a real kind of scary situation that just barely beginning to scratch the surface, I want you to get share some of your thoughts on deepfakes. >> I'm going to think you made a good point at the start. There's a lot of cute and funny stuff out there, there's a lot of fake political stuff you see. So is it seen as being humorous some people are sharing it a lot. But there is a darker side that's going to happen to deepfakes, because a lot of the things that you see today that go out on video, the reason that it is what it is, is because you're very familiar with the person that you're seeing in that video. Is a famous politician, is a movie star, and they're saying something that's out of character or funny and that's it. But what if that was actually the Chief Financial Officer of a major company, where the company appears to have launched a video, very close to the bell ringing on the stock market, that makes some kind of announcement about product or delay or something to do with their quarterly figures or something like that? You know that one minute video, could do a huge amount of damage to that organization. It could that somebody's looking to take advantage of a dip at that point, video goes out, their stocks going to dip, buy it out, then they could profit, but it all could also be much darker. It could be somebody who's trying to do that to actually damage their business. >> So, would you define a very good text base phishing spear phishing as a deepfake, where they've got enough data, where they're, the relevance of the topic is so spot on, the names that are involved in the text are so spot on 'cause they've done their homework, and the transactions that they're suggesting, are really spot on and consistent with the behavior of the things that their target does each and every day. >> So I'm not sure I defined that as a deepfake yet, obviously you've got two types of a phish, you've got a spear phish, which is the the perfected version, the work has gone into target, you as a specific, high value individual for some reason in your organization, but what we are seeing is in the same way that deepfakes are leveraging technology to be able to manipulate somebody, things like the fact that we're all on Instagram, we're all on Facebook, we're all on Twitter, means that social manipulation is a lot easier for the bad guys to be able to create, phishing campaigns that appear to be very much more targeted, they can create emails because they know you've got a dog. They know roughly where you live, because you're this information is coming up in pictures and it's a metro on the internet. And so they can generate automated messaging and emails and things that are going to go out. That will appear to be from whomever you expect to receive it from, using words that you think that only they would know about to make that appear to be more realistic. >> Right. >> And that's actually something, we sort of seen the start of that, but still the thing to spot is that the grammar is very often not very good in these if they haven't perfected the language side of it. >> But that's coming right, but that's coming right. >> But they all getting much more accurate yeah. >> We is an automated transcription service to do all the transcription on these videos. And you know, It's funny you can you can pay for the machine or you can pay for the human, we do both. But it's amazing, even only in the last six months to see the Delta shrink between the machine generated and the person generated. And this is even in, you know, pretty technical stuff that we get in very specific kind of vocabulary around the tech conferences that we cover. And the machines are catching up very, very fast. >> They very much are. but then if you think about, this is not new. What's happened, it's been happening in the background for a while things like quite a lot of legal work is done. If you look at a state agency, for example, conveyancing it's not uncommon for the conveyancing to be done using machine learning and using computer generated documentation because it's within a framework. But of course, the more it does that, the more that it learns. And then that software can more easily be applied to other other areas to be able to do that accurately. >> Right. So another big topic that gets a lot of conversation is passwords. You know, it's been going on forever, and now we're starting to get The two factor authentication, you know, the new Apple phones, you can look at it and identify it, you say now you have kind of biometrics. But that can all be hacked, too, right? It's just a slightly different, a slightly different method. But, you know, even those, the biometric is not at all. >> Well. >> That's secure. >> I think the thing is, you see that when you're logging into something, there's two pieces of information you need. There's there's what you are you as a person and then there's the thing that you know, a lot of people confuse biometrics, thinking of biometric authentication is their password, we're actually the biometric is is the them. And so you still should back things with strong passwords, you still should have that behind it. Because if somebody does get through the biometric that shouldn't automatically just give them access to absolutely everything. It's you know, these are technologies that are provided to make things easier to make it so that you can have less strong passwords so that so that you do know where you're storing information. But People over people tend to rely on them too much, it is still very, very important to use strong passwords to think about the process for how you want to do that. Taking statements and then turning those statements into strange sentences that only you understand maybe having your own code to do that conversion. So that you have a very strong password that nobody's ever going to pick up, right? We know that common passwords, unfortunately, are still 1234567 password, its horrific. >> I know, i saw some article that you're quoted in and it had the worst 25 passwords for 2018 and 2019. And it's basically just pick and pick a string. >> They just don't change. >> But you know, but it's interesting cause, you know, having a hard Prat, you know, it's easy to make, take the time and go ahead and create that, that that strong password. But then, you know, three months later. Salesforce keeps making me do a new one or the bank keeps making me do a new one. What's your opinion in some of these kind of password managers? Because to me, it seems like okay, well, I might be doing a great job creating some crazy passwords for the specific accounts. But what if I could hacked on that thing right now they have everything in the same a single place. >> Yeah. So this is where things like two factor authentication become really, really important. So I use passwords manager. And I've been I'm very, very careful with the how my passwords are created and what goes in there so that i know where certain passwords are created for certain types of account and certain complexities. But I also turned on two factor. And if somebody does try to go into my online password account, I will get an alert to say that they've tried to do that a single failed authentication and I will get an alert to say that they've done it an authentication that happens where I'm not I you know, then I will get a note say I've done that. So this is where there's that second factor actually becomes very important. If you have something that gives you the option to use two factor authentication. Use it. >> Use it. >> You know, it may, you know, we it is a pain when you're trying to do something with your credit card and you have to do One time text. But it'd be more of a pain if you didn't and somebody else was to use it. And to fill it up nicely for you wouldn't right. >> Right. You know, it's funny part of the keynote from Rowan was talking about, you know, as a profession, spending way too much time thinking about the most kind of crazy bizarre, sophisticated attacks. At the at the fault of, you know, not necessarily paying attention to the basics and the basics is where still a lot of the damage was done right. >> You know what? This is the thing and then there's, you know, there's a, there's a few things in our industry. So exactly what you just said. Everybody seems to believe that they're going to be the target of the next really big complex, major attack. The reality is they aren't. And the reality is that they've been hit by the basic slight ransomware, phishing spearphishing credential stuffing all these attacks are hitting them all the time. And so they need to have those foundational elements in place against those understanding what those are and not worry about the big stuff because the reality is if your organization is going to be hit by a nation state level complex attack. Or you can do fight against that as well, it's going to happen. And that's the thing with a lot of the buzzwords that we see in in cyber today as Matt. >> And and with smaller companies SMB's, I mean is really their only solution to go with, you know, cloud providers and other types of organizations and have the resources to get the people and the systems and the processes to really protect them because you can't expect you to just flowers down down off fourth street to be have any type of sophistication needed. But as soon as you plug that server in with a website, you're instantly going to get, get attacked , right. >> So the thing is, you can expect that, that guy to be an expert. He's not going to be an expert in cybersecurity and the cost of hiring someone is going to outweigh the value who's getting back. My recommendation that case is to look for organizations that can actually help you to become more cyber resilience. So an organization that I work with, it's actually UK and US basis, the global cyber alliance. They actually produce a small business toolkit. So it's a set of tools which are not chargeable is put together. And some of it might be a white paper, a set of recommendations, it might actually be a vendor developed tool that they can use to download to check the vulnerabilities or something like that. But what it does is it provides a framework for them. So they go through and say, Okay, yeah, I get this. This is English, simple language. And it helps to protect me as a small business owner, not a massive enterprise where actually none of those solutions fits what i one's to. So that's my recommendation to small businesses, look for these types of organization, work with someone like that, listen to what they're doing and learn cyber from them. >> Yeah, that's good tip. I want to, kind of of double click on that. So that makes sense when it's easy to measure your ROI on a small business. I just can't afford the security pros. >> Yeah. >> For bigger companies when they're doing their budgeting for security. To me, it's always a really interesting as i can, it's insurance at some point, you know, wouldn't be great if i could ensure 100% coverage, but we can't. And there's other needs in the business beyond just investing in, in cyber security, how should people think about the budgets relative to, as you just said, the value that they're trying to protect? How do you help people think about their cyber security budgets and allocations. >> So then there needs to be and this is happening, a change in how the conversation works between the security team and the board who own those budgets. What tends to happen today is that there's a cyber team wants to provide the right information to the board that's going to make them see how good what they're doing is and how successful they are and justifies the spend that they've made and also justifies the future investments that they're going to need to make. But very often, that falls back on reporting on big numbers, statistics, we blocked billions of threats. We turned away millions of pieces of malware. Actually, that conversation needs to narrow down and the team should be saying, Okay, so in the last two months, we had Five attacks that came in, we actually dealt with them by doing this, this is the changes that we've made, this is what we've learned. However, if we had had this additional or this switched on, then we would have been more successful or we'd have been faster or we could have turned down the time on doing that. Having that risk and compliance type conversation is actually adding value to the security solutions they've got and the board understand that they get that conversation, you're going to be happy to engage. This is happening, this is something that is happening. And it will, it's going to get better and better. But that's that's where things need to go. >> Right. Cause the other hard thing is it's kind of like we've joked earlier, it's kind of like an offensive lineman, they do a great job for 69 plays. And on the seventh seventh play, they get a holding call. That's all anybody sees . And you know, there's, again, that was part of robots, keynote that we can't necessarily brag about all the DDoS taxes that we stopped cause we can't let the bad guys kind of know where we're, we're being successful. So it's a little bit of a challenge in tryna show the ROI. Show the value when you can't necessarily raise your hand and say, hey, we stopped the 87. Tax. >> Yeah, >> Cause it's only the 88. That really is the one that that showed up in the Wall Street Journal. >> I think the thing with that is when organizations are looking at security solutions, specifically, we're very aware of that. As you know, organizations struggle to get customer references, you'll see a lot of the references are major financial, large manufacturing organization, because companies don't want to step up and say, I implemented security, they did this because the reverse of that is, she didn't have it before then >> Right right, or we'll go in that door not that door. >> Yeah and so, but there are a lot of good testing organizations out there that actually do take the security solutions, and run them through very, very stringent tests and then report back on the success of those tests. So you know, we work closely with NSX labs, for example, we've had some very good reports that have come out from there, where they do a drill down into how fast how much, how many, and then that's the kind of You can then take to the board. That's the kind of thing that you can publicize to say, the reason that we're using Juniper X or x firewalls is because in this report, this is what it said, this is how good that product was. And then you're not admitting a weakness. You're actually saying we're strong because we did this work in this research background. >> Right, very different kind of different approach. >> Yeah, yeah. >> Yeah well, Lawrence really enjoyed the conversation. We'll have to leave it here. But I think you have no shortage of job security, even though we will know everything in 2020 with the benefit of hindsight. >> Really, yeah thank you very much for that. >> All right. Thanks a lot. Alright, he's Lawrence. I'm Jeff. You're watching the cube. We're at RSA 2020 in Moscone. Thanks for watching. We'll see you next time.

>> Narrator: Live from San Francisco it's theCUBE covering RSA Conference 2020 San Francisco brought to you by SiliconANGLE media. >> Welcome back, everybody. Jeff Frick here with theCUBE. We are at the RSA 2020, a really special segment. As you can tell it's really quiet here, it's not like normal CUBE action, we are here before the expo hall even opens on Thursday morning with a very special guest, we pulled them away from a crazy busy week if not more, it's Rohit Ghai the president of RSA, Rohit great to see you again. >> Always a pleasure, thanks Jeff. >> Absolutely, so I was really looking forward to this, I was really impressed by the opening keynotes, first it rolled out George Takei, that's a pretty bold move even more bold is to try to follow him up. >> Totally (laughing) >> So congratulations, and you know, that was pretty brave. >> I appreciate it, thank you. That was quite a, you know, quite a hurdle to got to follow George Takei. >> Right, and I just want to get kind of these other things that were kind of bubbling above the surface out of the way you know, a big piece of news, I think a week it came out before the show is that RSA was sold to Symphony I believe? >> Rohit: Symphony Technology Group. >> Right, so give us a little bit of the story there. >> Absolutely, so you know we entered into a definitive agreement, Symphony Technology Group acquiring RSA from Dell Technologies. What this does is this it basically clarifies the swim lanes for Dell Technologies to focus on intrinsic security and RSA can focus on managing digital and cyber risk, and you know, we are excited about the opportunity to become agile and independent and you know, kind of play in a smaller company setting to pursue our future, so we are super excited to be part of Symphony. >> Yeah, that's great, and the other thing that's kind of a pall, I mean just to put it out there is the corona virus thing. And you know, Mobile World Congress, a completely different show but a big show, probably the first big show of our industry this year was canceled. A hundred thousand plus people, so I just am just wondering if you can share kind of what were some of your thoughts and the team's thoughts 'cause we were all curious to see well how is this going to happen, there was a couple of drop outs but I think it's been a very good week. >> It has been a great week, you know what I'll say is it was a demonstration of resilience on part of the attendees, you know when we analyzed the situation what we noted was about 82 plus percent of our attendees are from the Americas right, so there was a core set of attendees that were perhaps not as impacted in terms of travel, et cetera, so we decided to move forward, we've been in close collaboration with the CDC and the mayor's office right here, Major London Breed's office right here is SF to make sure it's going to be a safe event for everyone and you know, the team put together a great kind of set of measures to make sure everyone has hand sanitizer. >> Great, great. >> And you know, we made sure we did what was needed to manage the risk and ensure resilience through this sort of you know very global risk that is playing out, so very proud of the team, and we garnered 40 thousand plus attendees despite you know, despite the coronavirus issue. >> You know, good job I am sure it was touch and go and a real sensitive situation and I can tell you a lot of other people and event organizers you know, were getting ready to head into a very busy event season, it's what we do and so, you know nice kind of lead indicator from you to execute with caution. >> I appreciate it, thank you. >> So let's jump into the fun stuff. So your key note was not really talking that much about bad guys and technology and this and that, you talked about story telling and you got very much into kind of the human element, which is the theme this year, but really the role of stories, the importance of stories, and most importantly for the security industry to take back their story and not let it get away from them. >> You summed it up really well Jeff, and you know what I said is hey if the theme of the conference is the human element, let's explore what intrinsically makes us human and the point, you know you've all know that it is stories that makes us human and I feel we've lost control of the narrative as an industry and as such we need to take that back and make sure we clarify the role of all the human characters in our story because until we do that, until we change our story we have no shot at changing our reality. >> Right, but you're kind of in a weird spot right, it's the classic spy dilemma. You can't necessarily tell people what you know because then they'll know that you know it and you might not be able to get more or better information down the road, so as you said in you keynote you don't necessarily have the ability to celebrate your wins, and a DDoS attack thwarted doesn't make the news. I keep thinking it's like ref in a game or like a offensive lineman in football you only hear about them on that one play when they get the holding call, not the 70 other plays were they did their job. >> Rohit: Totally, totally. >> So it's a unique challenge though >> It is, it is a challenge, it is not an easy problem and you know, there is a couple of recipes that I put out there for us to consider as an industry is you know, recipe one is we can celebrate our successes at a collective level right so, just like we put out breach reports, et cetera, in terms of what the statistics are, where the breaches are animating from we can talk about defensive strategies that are working at a collective level as an industry and share that sort of best practices recipes to win, that would be a fine start. I think another area, another point that I made was that we don't have to win for the hacker to lose. 71% of the breaches were motivated by financial gains, right, and as such if we, despite breaches, which is not a win for us, if we deny financial gain to the hackers we make them lose and they are subject to the same laws of economics, they have a profit and loss statement, they are spending resources for gain and when we deny them gain we make them lose, so those are a couple of ideas on how we can begin to change the narrative. >> Right. So the other piece of the human part is the rise of the bots, right, and the raise of AI and the rise of these increasingly smart and sophisticated machines. I think I saw one of those reports that we talk about on air was you know that people are an increasingly targeted group we hear it all the time, we hear about social engineering. As that gets more complicated, how does the role of people change? 'Cause clearly they can't monitor tens and tens and hundreds of thousands of concurrent attacks all the time. >> Absolutely, so you know the bad guys are using AI you know I cited the example of a deep fake audio clip that actually duped the CEO into initiating a wire transfer so they are using all these sophisticated attacks so to your point, we cannot rely on the end user to discern through these very sophisticates. It's unfair for us to think of them as the first line of defense, we have to on the IT side, we have to bring in technology, make the technology more usable, so you don't have to pay attention to this one millimeter by one millimeter lock at the corner of the browser to realize whether a web interaction is safe or not. We need to make more usable software, we need to do a better job of managing and reducing vulnerabilities to reduce the attack surface so IT has to step up in that regard, and then on the security teams I think they have to step up to use AI to detect bot initiated attacks so we are not leaning on the human to discern what is an anomalous interaction and what could be a phishing or a smishing attack, et cetera, you know we need to bring AI to fight the good fight on our behalf. >> Right. So the other kind of angle on that I thought was really interesting, Wendy's keynote, a couple of keynotes after yours from Cisco talked about, you know, a theme we see over and over in tech which is really kind of the democratization of security and get it out of just the hallowed halls of the super billion CSOCs and technologists that are just security and open it up to everybody so make them part of the solution and not those pesky people that keep clicking on links that they are not supposed to. >> Absolutely. She did a great job of kind of making that point and you know the way I think about it is again we need to move from a culture of elitism to a culture of inclusion. Until we really get the steaming going, not just within the security professionals which we are doing a better job of certainly in the industry, but we have to team with the user, the IT and the business teams in order to have a shot at tipping the balance in our favor. >> Yeah, it's really funny 'cause that kind of democratization theme is something that we see kind of across many levels of technology, whether it's in big data, can get away from the data scientists, in doing your own reports, in having access to your own marketing material and you know, so it's kind of funny that now we are just hearing it here I guess the last bastion of we're the smartest people in the room, no no, you need to use all the brain power. >> All the brain power. I use the phrase let's stop being STEM snobs and let's be more inclusive, and you know garner the entire spectrum of the diverse talent pool that we have available and you know making the point, perhaps a provocative point, that the cyber talent gap, a bit of it might be actually self-inflicted because we have been in this sort of elitism mindset. >> Right, and I think one of the themes that you talked about in you keynote was because of kind of the elite mindset we only want to focus on the elite challenges and in fact it's not the hardest challenges that are necessarily the most dangerous or the ones that are more frequently used, it doesn't have to be the craziest hardest way in. >> It absolutely does not. The point I made was preparing for the worse does not prepare you for the likely and the statistics are overwhelming. 60% of the breaches were on the back of six stolen credentials. That's a pretty table stakes basic issue that ought to be just taken off the table, and if we take care of the basics then we can focus our energy on the corner cases but let's first prepare for the likely before we get to the worst situations. >> Right. So Rohit I'm just curious to get your take as you have been here for the last couple of days, you know you did a whole lot of work getting into that keynote and getting this thing up and off the ground but you've had a couple of days to be here walked around, talked to a lot of customers and clients, partners, I wonder if there is anything that's kind of come up as a theme that you either didn't expect or kind of reinforced some of thoughts that you had coming into this week. >> Absolutely. I think if I would've net it out Jeff what I'm sensing is there is a whole movement to shift security left, which is this whole idea of IT stepping up as the first line of defense, reduce cyber exposure, take care of patching, multi-factor authentication, reduce the attack surface intrinsic security right so DevOps and SecDevOps take care of it right up front before the apps even get built right, then there is another movement to shift things right which is take care of the new aspects of the attack surface right, what the hacker always take advantage of are the areas where they sense we are unprepared and for a long time they've seen us being unprepared in terms of reducing the attack surface and then they go after the new aspects of the attack surface and what are those? IT, IoT, OT, data as an attack surface and the Edge right, so these are areas were there is a lot of activity, a lot of innovation, you know, on the floor here if you walk the corners shifting left shifting right as in all the new aspects of the attack surface. I am seeing a lot of conversations, a lot of innovation is that area. >> Yeah. Well, there's certainly no shortage of innovation in the companies here and in fact I think it's probably one of the biggest challenges that I think of from a virus perspective is to walk this floor and to figure it all out 'cause I don't know how many thousand of vendors there are but there's really big ones and there is lot's of little ones like you said tucked in the corner in kind of the cutting edge of the innovation. What advice do you give to people who is their first time coming to RSA? >> Yes, I think you know, it's a huge challenge for customers, there's 14 of every category. I think the customers what they have to see is they have to think about the recipe rather they have to focus not on the tool but the concept behind the tool, and think about the architecture right and they should seek out vendors that take this platform approach. It is, you know, the market hasn't consolidated that much where they can just go to a few vendors but when they build that architecture they should choose vendors that behave well as a puzzle piece in the jigsaw puzzle that our customers are having to assemble together right, that they are investing in the API integrations on the edges so they can slot in and be part of a broader solution. That's a key, key criteria that customers should utilize in their selection of the vendors. >> Yes, that's good. That's good advice, and they should be listening. So Rohit, thanks again for your time. Congratulations on a week and I hope you get that weekend of absolutely nothing coming up in just a couple of days that you talked about. >> I absolutely do. The joke I made was, you know, the only time I'm okay being labeled as useless is the weekend after RSA conference. So, I fully look forward to being useless over this weekend, it's been a great week and thank you again for having me. >> All right, two more days, 48 hours. All right, thanks again. He's Rohit, I'm Jeff, you're watching theCUBE. We're at RSA 2020, the year we're going to know everything with the benefit of hindsight. We're not quite there yet but we're trying yo get a little closer. Thanks for watching, we'll see you next time. (upbeat music)

>> Narrator: Live from San Francisco. It's theCUBE, covering RSA Conference 2020, San Francisco. Brought to you by, SiliconANGLE Media. >> Welcome back everyone. CUBE coverage here in Moscone in San Francisco for RSA, 2020. I'm John Furrier host of theCUBE. We've got a great guest here talking about cybersecurity and the impact with AI and the role of data. It's always great to have Derek Manky on Chief Security Insights Global Threat Alliances with FortiGuard Lab, part of Fortinet, FortiGuard Labs is great. Great organization. Thanks for coming on. >> It's a pleasure always to be here-- >> So you guys do a great threat report that we always cover. So it covers all the bases and it really kind of illustrates state of the art of viruses, the protection, threats, et cetera. But you're part of FortiGuard Labs. >> Yeah, that's right. >> Part of Fortinet, which is a security company, public. What is FortiGuard Labs? What do you guys do, what's your mission? >> So FortiGuard Labs has existed since day one. You can think of us as the intelligence that's baked into the product, It's one thing to have a world-class product, but you need a world-class intelligence team backing that up. We're the ones fighting those fires against cybercrime on the backend, 24/7, 365 on a per second basis. We're processing threat intelligence. We've got over 10 million attacks or processing just per minute, over a hundred billion events, in any given day that we have to sift through. We have to find out what's relevant. We have to find gaps that we might be missing detection and protection. We got to push that out to a customer base of 450,000 customers through FortiGuard services and 5 million firewalls, 5 million plus firewalls we have now. So it's vitally important. You need intelligence to be able to detect and then protect and also to respond. Know the enemy, build a security solution around that and then also be able to act quickly about it if you are under active attack. So we're doing everything from creating security controls and protections. So up to, real time updates for customers, but we're also doing playbooks. So finding out who these attackers are, why are they coming up to you. For a CSO, why does that matter? So this is all part of FortiGuard Labs. >> How many people roughly involved ? Take us a little inside the curtain here. What's going on? Personnel size, scope. >> So we're over 235. So for a network security vendor, this was the largest global SOC, that exists. Again, this is behind the curtain like you said. These are the people that are, fighting those fires every day. But it's a large team and we have experts to cover the entire attack surface. So we're looking at not just a viruses, but we're looking at as zero-day weapons, exploits and attacks, everything from cyber crime to, cyber warfare, operational technology, all these sorts of things. And of course, to do that, we need to really heavily rely on good people, but also automation and artificial intelligence and machine learning. >> You guys are walking on a tight rope there. I can only imagine how complex and stressful it is, just imagining the velocity alone. But one of the trends that's coming up here, this year at RSA and is kind of been talking about in the industry is the who? Who is the attacker because, the shifts could shift and change. You got nation states are sitting out there, they're not going to have their hands dirty on this stuff. You've got a lot of dark web activity. You've got a lot of actors out there that go by different patterns. But you guys have an aperture and visibility into a lot of this stuff. >> Absolutely. >> So, you can almost say, that's that guy. That's the actor. That's a really big part. Talk about why that's important. >> This is critically important because in the past, let's say the first generation of, threat intelligence was very flat. It was to watch. So it was just talking about here's a bad IP, here's a bad URL, here's a bad file block hit. But nowadays, obviously the attackers are very clever. These are large organizations that are run a lot of people involved. There's real world damages happening and we're talking about, you look at OT attacks that are happening now. There's, in some cases, 30, $40 million from targeted ransom attacks that are happening. These people, A, have to be brought to justice. So we need to understand the who, but we also need to be able to predict what their next move is. This is very similar to, this is what you see online or CSI. The police trynna investigate and connect the dots like, plotting the strings and the yarn on the map. This is the same thing we're doing, but on a way more advanced level. And it's very important to be able to understand who these groups are, what tools they use, what are the weapons, cyber weapons, if you will, and what's their next move potentially going to be. So there's a lot of different reasons that's important. >> Derek, I was riffing with another guest earlier today about this notion of, government protection. You've got a military troops drop on our shores and my neighborhood, the Russians drop in my neighborhood. Guess what, the police will probably come in, and, or the army should take care of it. But if I got to run a business, I got to build my own militia. There's no support out there. The government's not going to support me. I'm hacked. Damage is done. You guys are in a way providing that critical lifeline that guard or shield, if you will, for customers. And they're going to want more of it. So I've got to ask you the hard question, which is, how are you guys going to constantly be on the front edge of all this? Because at the end of the day, you're in the protection business. Threats are coming at the speed of milliseconds and nanoseconds, in memory. You need memory, you need database. You've got to have real time. It's a tsunami of attack. You guys are the front lines of this. You're the heat shield. >> Yes, absolutely. >> How do you take it to the next level? >> Yeah, so collaboration, integration, having a broad integrated platform, that's our bread and butter. This is what we do. End-to-end security. The attack surface is growing. So we have to be able to, A, be able to cover all aspects of that attack surface and again, have intelligence. So we're doing sharing through partners. We have our core intelligence network. Like I said, we're relying heavily on machine learning models. We're able to find that needle in the haystack. Like, as I said earlier, we're getting over a hundred billion potential threat events a day. We have to dissect that. We have to break it down. We have to say, is this affecting endpoint? Is this effect affecting operational technology? What vertical, how do we process it? How do we verify that this is a real threat? And then most importantly, get that out in time and speed to our customers. So I started with automation years ago, but now really the way that we're doing this is through broad platform coverage. But also machine learning models for and-- >> I want to dig into machine learning because, I love that needle in the haystack analogy, because, if you take that to the next step, you got to stack a needles now. So you find the needle in the haystack. Now you got a bunch of needles, where do you find that? You need AI, you got to have some help. But you still got the human component. So talk about how you guys are advising customers on how you're using machine learning and get that AI up and running for customers and for yourselves. >> So we're technology people. I always look at this as the stack. The stack model, the bottom of the stack, you have automation. You have layer one, layer two. That's like the basic things for, feeds, threat feeds, how we can push out, automate, integrate that. Then you have the human. So the layer seven. This is where our human experts are coming in to actually advise our customers. We're creating a threat signals with FortiGuard Labs as an example. These are bulletins that's a quick two to three page read that a CSO can pick up and say, here's what FortiGuard Labs has discovered this week. Is this relevant to my network? Do I have these protections in place. There's also that automated, and so, I refer to this as a centaur model. It's half human half machine and, the machines are driving a lot of that, the day to day mundane tasks, if you will, but also finding, collecting the needles of needles. But then ultimately we have our humans that are processing that, analyzing it, creating the higher level strategic advice. We recently, we've launched a FortiAI, product as well. This has a concept of a virtual-- >> Hold on, back up a second. What's it called? >> FortiAI. >> So it's AI components. Is it a hardware box or-- >> This is a on-premise appliance built off of five plus years of learning that we've done in the cloud to be able to identify threats and malware, understand what that malware does to a detailed level. And, where we've seen this before, where is it potentially going? How do we protect against it? Something that typically you would need, four to five headcount in your security operations center to do, we're using this as an assist to us. So that's why it's a virtual analyst. It's really a bot, if you will, something that can actually-- >> So it's an enabling opportunity for the customers. So is this virtual assistant built into the box. What does that do, virtual analyst. >> So the virtual analyst is able to, sit on premises. So it's localized learning, collect threats to understand the nature of those threats, to be able to look at the needles of the needles, if you will, make sense of that and then automatically generate reports based off of that. So it's really an assist tool that a network admin or a security analyst was able to pick up and virtually save hours and hours of time of resources. >> So, if you look at the history of like our technology industry from a personalization standpoint, AI and data, whether you're a media business, personalization is ultimately the result of good data AI. So personalization for an analyst, would be how not to screw up their job. (laughs) One level. The other one is to be proactive on being more offensive. And then third collaboration with others. So, you starting to see that kind of picture form. What's your reaction to that? >> I think it's great. There's stepping stones that we have to go through. The collaboration is not always easy. I'm very familiar with this. I mean I was, with the Cyber Threat Alliance since day one, I head up and work with our Global Threat Alliances. There's always good intentions, there's problems that can be created and obviously you have things like PII now and data privacy and all these little hurdles they have to come over. But when it works right together, this is the way to do it. It's the same thing with, you talked about the data naturally when he started building up IT stacks, you have silos of data, but ultimately those silos need to be connected from different departments. They need to integrate a collaborate. It's the same thing that we're seeing from the security front now as well. >> You guys have proven the model of FortiGuard that the more you can see, the more visibility you can see and more access to the data in real time or anytime scale, the better the opportunity. So I got to take that to the next level. What you guys are doing, congratulations. But now the customer. How do I team up with, if I'm a customer with other customers because the bad guys are teaming up. So the teaming up is now a real dynamic that companies are deploying. How are you guys looking at that? How is FortiGuard helping that? Is it through services? Is it through the products like virtual assistant? Virtual FortiAI? >> So you can think of this. I always make it an analogy to the human immune system. Artificial neural networks are built off of neural nets. If I have a problem and an infection, say on one hand, the rest of the body should be aware of that. That's collaboration from node to node. Blood cells to blood cells, if you will. It's the same thing with employees. If a network admin sees a potential problem, they should be able to go and talk to the security admin, who can go in, log into an appliance and create a proper response to that. This is what we're doing in the security fabric to empower the customer. So the customer doesn't have to always do this and have the humans actively doing those cycles. I mean, this is the integration. The orchestration is the big piece of what we're doing. So security orchestration between devices, that's taking that gap out from the human to human, walking over with a piece of paper to another or whatever it is. That's one of the key points that we're doing within the actual security fabric. >> So that's why silos is problematic. Because you can't get that impact. >> And it also creates a lag time. We have a need for speed nowadays. Threats are moving incredibly fast. I think we've talked about this on previous episodes with swarm technology, offensive automation, the weaponization of artificial intelligence. So it becomes critically important to have that quick response and silos, really create barriers of course, and make it slower to respond. >> Okay Derek, so I got to ask you, it's kind of like, I don't want to say it sounds like sports, but it's, what's the state of the art in the attack vectors coming in. What are you guys seeing as some of the best of breed tax that people should really be paying attention to? They may, may not have fortified down. What are SOCs looking at and what are security pros focused on right now in terms of the state of the art. >> So the things that keep people up at night. We follow this in our Threat Landscape Report. Obviously we just released our key four one with FortiGuard Labs. We're still seeing the same culprits. This is the same story we talked about a lot of times. Things like, it used to be a EternalBlue and now BlueKeep, these vulnerabilities that are nothing new but still pose big problems. We're still seeing that exposed on a lot of networks. Targeted ransom attacks, as I was saying earlier. We've seen the shift or evolution from ransomware from day to day, like, pay us three or $400, we'll give you access to your data back to going after targeted accounts, high revenue business streams. So, low volume, high risk. That's the trend that we're starting to see as well. And this is what I talk about for trying to find that needle in the haystack. This is again, why it's important to have eyes on that. >> Well you guys are really advanced and you guys doing great work, so congratulations. I got to ask you to kind of like, the spectrum of IT. You've got a lot of people in the high end, financial services, healthcare, they're regulated, they got all kinds of challenges. But as IT and the enterprise starts to get woke to the fact that everyone's vulnerable. I've heard people say, well, I'm good. I got a small little to manage, I'm only a hundred million dollar business. All I do is manufacturing. I don't really have any IP. So what are they going to steal? So that's kind of a naive approach. The answer is, what? Your operations and ransomware, there's a zillion ways to get taken down. How do you respond to that. >> Yeah, absolutely. Going after the crown jewels, what hurts? So it might not be a patent or intellectual property. Again, the things that matter to these businesses, how they operate day to day. The obvious examples, what we just talked about with revenue streams and then there's other indirect problems too. Obviously, if that infrastructure of a legitimate organization is taken over and it's used as a botnet and an orchestrated denial-of-service attack to take down other organizations, that's going to have huge implications. >> And they won't even know it. >> Right, in terms of brand damage, has legal implications as well that happened. This is going even down to the basics with consumers, thinking that, they're not under attack, but at the end of the day, what matters to them is their identity. Identity theft. But this is on another level when it comes to things to-- >> There's all kinds of things to deal with. There's, so much more advanced on the attacker side. All right, so I got to ask you a final question. I'm a business. You're a pro. You guys are doing great work. What do I do, what's my strategy? How would you advise me? How do I get my act together? I'm working the mall every day. I'm trying my best. I'm peddling as fast as I can. I'm overloaded. What do I do? How do I go the next step? >> So look for security solutions that are the assist model like I said. There's never ever going to be a universal silver bullet to security. We all know this. But there are a lot of things that can help up to that 90%, 95% secure. So depending on the nature of the threats, having a first detection first, that's always the most important. See what's on your network. This is things where SIM technology, sandboxing technology has really come into play. Once you have those detections, how can you actually take action? So look for a integration. Really have a look at your security solutions to see if you have the integration piece. Orchestration and integration is next after detection. Finally from there having a proper channel, are there services you looked at for managed incident response as an example. Education and cyber hygiene are always key. These are free things that I push on everybody. I mean we release weekly threat intelligence briefs. We're doing our quarterly Threat Landscape Reports. We have something called threat signals. So it's FortiGuard response to breaking industry events. I think that's key-- >> Hygiene seems to come up over and over as the, that's the foundational bedrock of security. >> And then, as I said, ultimately, where we're heading with this is the AI solution model. And so that's something, again that I think-- >> One final question since it's just popped into my head. I wanted, and that last one. But I wanted to bring it up since you kind of were, we're getting at it. I know you guys are very sensitive to this one topic cause you live it every day. But the notion of time and time elapsed is a huge concern because you got to know, it's not if it's when. So the factor of time is a huge variable in all kinds of impact. Positive and negative. How do you talk about time and the notion of time elapsing. >> That's great question. So there's many ways to stage that. I'll try to simplify it. So number one, if we're talking about breaches, time is money. So the dwell time. The longer that a threat sits on a network and it's not cleaned up, the more damage is going to be done. And we think of the ransom attacks, denial-of-service, revenue streams being down. So that's the incident response problem. So time is very important to detect and respond. So that's one aspect of that. The other aspect of time is with machine learning as well. This is something that people don't always think about. They think that, artificial intelligence solutions can be popped up overnight and within a couple of weeks they're going to be accurate. It's not the case. Machines learn like humans too. It takes time to do that. It takes processing power. Anybody can get that nowadays, data, most people can get that. But time is critical to that. It's a fascinating conversation. There's many different avenues of time that we can talk about. Time to detect is also really important as well, again. >> Let's do it, let's do a whole segment on that, in our studio, I'll follow up on that. I think it's a huge topic, I hear about all the time. And since it's a little bit elusive, but it kind of focuses your energy on, wait, what's going on here? I'm not reacting. (laughs) Time's a huge issue. >> I refer to it as a latency. I mean, latency is a key issue in cybersecurity, just like it is in the stock exchange. >> I mean, one of the things I've been talking about with folks here, just kind of in fun conversation is, don't be playing defense all the time. If you have a good time latency, you going to actually be a little bit offensive. Why not take a little bit more offense. Why play defense the whole time. So again, you're starting to see this kind of mentality not being, just an IT, we've got to cover, okay, respond, no, hold on the ballgame. >> That comes back to the sports analogy again. >> Got to have a good offense. They must cross offense. Derek, thanks so much. Quick plug for you, FortiGuard, share with the folks what you guys are up to, what's new, what's the plug. >> So FortiGuard Labs, so we're continuing to expand. Obviously we're focused on, as I said, adding all of the customer protection first and foremost. But beyond that, we're doing great things in industry. So we're working actively with law enforcement, with Interpol, Cyber Threat Alliance, with The World Economic Forum and the Center for Cyber Security. There's a lot more of these collaboration, key stakeholders. You talked about the human to human before. We're really setting the pioneering of setting that world stage. I think that is, so, it's really exciting to me. It's a lot of good industry initiatives. I think it's impactful. We're going to see an impact. The whole goal is we're trying to slow the offense down, the offense being the cyber criminals. So there's more coming on that end. You're going to see a lot great, follow our blogs at fortinet.com and all-- >> Great stuff. >> great reports. >> I'm a huge believer in that the government can't protect us digitally. There's going to be protection, heat shields out there. You guys are doing a good job. It's only going to be more important than ever before. So, congratulations. >> Thank you. >> Thanks for coming I really appreciate. >> Never a dull day as we say. >> All right, it's theCUBE's coverage here in San Francisco for RSA 2020. I'm John Furrier, your host. Thanks for watching. (upbeat music)

>> Narrator: Live from San Francisco. It's the theCUBE covering RSA Conference 2020 San Francisco, brought to you by SiliconAngle Media. >> Alright, welcome to theCUBE coverage here at RSA Conference in San Francisco and Moscone Halls, theCUBE. I'm John Furrier, the host of theCUBE, in a cyber security is all about encryption data and also security. We have a very hot startup here, that amazing guest, Dr. Ellison Anne Williams, CEO and Founder of Enveil just recently secured a $10 million Series A Funding really attacking a real problem around encryption and use. Again, data ,security, analytics, making it all secure is great. Allison, and thanks for coming on. Appreciate your time. >> Thanks for having me. >> So congratulations on the funding before we get started into the interview talking about the hard news, you guys that are around the funding. How long have you guys been around? What's the funding going to do? What are you guys doing? >> Yeah, so we're about three and a half years old as a company. We just announced our Series A close last week. So that was led by C5. And their new US Funds The Impact Fund and participating. Other partners included folks like MasterCard, Capital One Ventures, Bloomberg, Beta 1843, etc. >> So some names jumped in C5 led the round. >> For sure. >> How did this get started? What was the idea behind this three years you've been actually doing some work? Are you going to production? Is it R&D? Is it in market? Give us a quick update on the status of product and solution? >> Yeah, so full production. For production of the product. We're in fact in 2.0 of the release. And so we got our start inside of the National Security Agency, where I spent the majority of my career. And we developed some breakthroughs in an area of technology called homomorphic encryption, that allows you to perform computations into the encrypted domain as if they were in the unencrypted world. So the tech had never existed in a practical capacity. So we knew that bringing seeds of that technology out of the intelligence community and using it to seed really and start the company, we would be creating a new commercial market. >> So look at this, right? So you're at the NSA, >> Correct >> Your practitioner, they're doing a lot of work in this area, pioneering a new capability. And did the NSA spin it out did they fund it was the seed capital there or did you guys bootstrap it >> No. So our seed round was done by an entity called Data Tribe. So designed to take teams in technologies that were coming out of the IC that wanted to commercialize to do so. So we took seed funding from them. And then we were actually one of the youngest company ever to be in the RSA Innovation Sandbox here in 2017, to be one of the winners and that's where the conversation really started to change around this technology called homomorphic encryption, the market category space called securing data in use and what that meant. And so from there, we started running the initial version of a product out in the commercial world and we encountered two universal reaction. One that we were expecting and one that we weren't. And the one that we were expecting is that people said, "holy cow, this actually works". Because what we say we do keeping everything encrypted during processing. Sounds pretty impossible. It's not just the math. And then the second reaction that we encountered that we weren't expecting is those initial early adopters turned around and said to us, "can we strategically invest in you?" So our second round of funding was actually a Strategic Round where folks like Bloomberg beta,Thomson Reuters, USA and Incue Towel came into the company. >> That's Pre Series A >> Pre Series A >> So you still moving along, if a sandbox, you get some visibility >> Correct. >> Then were the products working on my god is you know, working. That's great. So I want to get into before I get into some of the overhead involved in traditionally its encryption there always has been that overhead tax. And you guys seem to solve that. But can you describe first data-at-rest versus data-in-motion and data-in-user. data at rest, as means not doing anything but >> Yeah, >> In flight or in you so they the same, is there a difference? Can you just tell us the difference of someone this can be kind of confusing. >> So it's helpful to think of data security in three parts that we call the triad. So securing data at rest on the file system and the database, etc. This would be your more traditional in database encryption, or file based encryption also includes things like access control. The second area, the data security triad is securing data- in- transit when it's moving around through the network. So securing data at rest and in transit. Very well solution. A lot of big name companies do that today, folks like Talus and we partner with them, Talus, Gemalto, etc. Now, the third portion of the data security triad is what happens to that data when you go use or process it in some way when it becomes most valuable. And that's where we focus. So as a company, we secure data-in-use when it's being used or processed. So what does that mean? It means we can do things like take searches or analytics encrypt them, and then go run them without ever decrypting them at any point during processing. So like I said, this represents a new commercial market, where we're seeing it manifest most often right now are in things like enabling secure data sharing, and collaboration, or enabling secure data monetization, because its privacy preserving and privacy enabling as a capability. >> And so that I get this right, the problem that you solved is that during the end use parts of the triad, it had to be decrypted first and then encrypted again, and that was the vulnerability area. Look, can you describe kind of like, the main problem that you guys saw was that-- >> So think more about, if you've got data and you want to give me access to it, I'm a completely different entity. And the way that you're going to give me access to it is allowing me to run a search over your data holdings. We see this quite a bit in between two banks in the areas of anti-money laundering or financial crime. So if I'm going to go run a search in your environment, say I'm going to look for someone that's an EU resident. Well, their personal information is covered under GDPR. Right? So if I go run that search in your environment, just because I'm coming to look for a certain individual doesn't mean you actually know anything about that. And so if you don't, and you have no data on them whatsoever, I've just introduced a new variable into your environment that you now have to account for, From a risk and liability perspective under something like GDPR. Whereas if you use us, we could take that search encrypt it within our walls, send it out to you and you could process it in its encrypted state. And because it's never decrypted during processing, there's no risk to you of any increased liability because that PII or that EU resident identifier is never introduced into your space. >> So the operating side of the business where there's compliance and risk management are going to love this, >> For sure. >> Is that really where the action is? >> Yes, compliance risk privacy. >> Alright, so get a little nerdy action on this one. So encryption has always been an awesome thing depending on who you talk to you, obviously, but he's always been a tax associate with the overhead processing power. He said, there's math involved. How does homeomorphic work? Does it have problems with performance? Is that a problem? Or if not, how do you address that? Where does it? I might say, well, I get it. But what's the tax for me? Or is your tax? >> Encryption is never free. I always tell people that. So there always is a little bit of latency associated with being able to do anything in an encrypted capacity, whether that's at rest at in transit or in use. Now, specifically with homomorphic encryption. It's not a new area of encryption. It's been around 30 or so years, and it had often been considered to be the holy grail of encryption for exactly the reasons we've already talked about. Doing things like taking searches or analytics and encrypting them, running them without ever decrypting anything opens up a world of different types of use cases across verticals and-- >> Give those use case examples. What would be some that would be low hanging fruit. And it would be much more higher level. >> Some of the things that we're seeing today under that umbrella of secure data sharing and collaboration, specifically inside of financial services, for use cases around anti-money laundering and financial crimes so, allowing two banks to be able to securely collaborate with with each other, along the lines of the example that I gave you just a second ago, and then also for large multinational banks to do so across jurisdictions in which they operate that have different privacy and secrecy regulations associated with them. >> Awesome. Well, Ellison, and I want to ask you about your experience at the NSA. And now as an entrepreneur, obviously, you have some, you know, pedigree at the NSA, really, you know, congratulations. It's going to be smart to work there, I guess. Secrets, you know, >> You absolutely do. >> Brains brain surgeon rocket scientist, so you get a lot of good stuff. But now that you're on the commercial space, it's been a conversation around how public and commercial are really trying to work together a lot as innovations are happening on both sides of the fence there. >> Yeah. >> Then the ICC and the Intelligence Community as well as commercial. Yeah, you're an entrepreneur, you got to go make money, you got shareholders down, you got investors? What's the collaboration look like? How does the world does it change for you? Is it the same? What's the vibe in DC these days around the balance between collaboration or is there? >> Well, we've seen a great example of this recently in that anti-money laundering financial crime use case. So the FCA and the Financial Conduct Authority out of the UK, so public entity sponsored a whole event called a tech spread in which they brought the banks together the private entities together with the startup companies, so your early emerging innovative capabilities, along with the public entities, like your privacy regulators, etc, and had us all work together to develop really innovative solutions to real problems within the banks. In the in the context of this text spread. We ended up winning the know your customer customer due diligence side of the text brand and then at the same time that us held an equivalent event in DC, where FinCEN took the lead, bringing in again, the banks, the private companies, etc, to all collaborate around this one problem. So I think that's a great example of when your public and your private and your private small and your private big is in the financial services institutions start to work together, we can really make breakthroughs-- >> So you see a lot happening >> We see a lot happening. >> The encryption solution actually helped that because it makes sense. Now you have the sharing the encryption. >> Yeah. >> Does that help with some of the privacy and interactions? >> It breaks through those barriers? Because if we were two banks, we can't necessarily openly, freely share all the information. But if I can ask you a question and do so in a secure and private capacity, still respecting all the access controls that you've put in place over your own data, then it allows that collaboration to occur, whereas otherwise I really couldn't in an efficient capacity. >> Okay, so here's the curveball question for you. So anybody Startup Series today, but you really got advanced Series A, you got a lot of funding multiple years of operation. If I asked you what's the impact that you're going to have on the world? What would you say to that, >> Over creating a whole new market, completely changing the paradigm about where and how you can use data for business purposes. And in terms of how much funding we have, we have, we've had a few rounds, but we only have 15 million into the company. So to be three and a half years old to see this new market emerging and being created with with only $15 million. It's really pretty impressive. >> Yeah, it's got a lot of growth and keep the ownership with the employees and the founders. >> It's always good, but being bootstrap is harder than it looks, isn't it? >> Yeah. >> Or how about society at large impact. You know, we're living global society these days and get all kinds of challenges. You see anything else in the future for your vision of impact. >> So securing data and your supplies horizontally across verticals. So far we've been focused mainly on financial services. But I think healthcare is a great vertical to move out in. And I think there are a lot of global challenges with healthcare and the more collaborative that we could be from a healthcare standpoint with our data. And I think our capabilities enable that to be possible. And still respecting all the privacy regulations and restrictions. I think that's a whole new world of possibility as well. >> And your secret sauce is what math? What's that? What's the secret sauce, >> Math, Math and grit. >> Alright, so thanks for sharing the insights. Give a quick plug for the company. What are you guys looking to do? Honestly, $10 million in funding priorities for you and the team? What do you guys live in to do? >> So priorities for us? privacy is a global issue now. So we are expanding globally. And you'll be hearing more about that very shortly. We also have new product lines that are going to be coming out enabling people to do more advanced decisioning in a completely secure and private capacity. >> And hiring office locations DC. >> Yes. So our headquarters is in DC, but we're based on over the world, so we're hiring, check out our web page. We're hiring for all kinds of roles from engineering to business functionality >> And virtual is okay virtual hires school >> Virtual hires is great. We're looking for awesome people no matter where they are. >> You know, DC but primary. Okay, so great to have you gone. Congratulations for one, the financing and then three years of bootstrapping and making it happen. Awesome. >> Thank you. >> Thank you for coming ,appreciate it. So keep coming to your RSA conference in Moscone. I'm John Furrier. Thanks for watching more after this short break (pop music playing)

>> Narrator: Live from San Francisco, it's theCUBE, covering RSA Conference 2020 San Francisco. Brought to you by SiliconANGLE Media. >> Welcome back everyone. This is theCUBE's coverage from RSA Conference on Moscone South. I'm John Furrier, host of theCUBE. You know, cybersecurity is changing, and the next technology is right around the corner, and it's got to be invented somewhere, and of course Accenture Labs is part of it. Our next guest is Lisa O'Connor, Global Security R&D Lead for Accenture Labs. Lisa's working on some of those hard problems all around the world. Thank you for joining me today. Thanks for coming on. >> Thank you for having me. >> So, we always get the good scoop from Accenture, because you have a lot of smart people in that company. You know, they know their stuff. I know you got a huge analytics team. I've talked to Jean-Luc Chatelain before, and I know you got a massive amount of, deep bench of talent. But as you have to go do the applied R&D, and maybe some of the crazy ideas, you got to start thinking about where the puck is going to be. >> Absolutely. >> You got to understand that. Well, it's pretty clear to us that Cloud is certainly there. Palo Alto Networks had a disappointing earnings yesterday, because their on-premises business is shifting to the Cloud. You're seeing hybrid operating model and multicloud for the enterprise, but now you got global challenges. >> We absolutely do. >> Huge, so what are you guys working on that's coming? Tell us. >> So we're working on lots of exciting things, and Cloud is one of them. But, some of the things I'm so passionate about in labs, and I have the best job at Accenture. Don't tell anyone. (laughs) I do. So, we are working on, like Jean-Luc is working on applied intelligence, we are working on robust AI. So, when we think about AI in the future, how do we feel that, and know that it's okay? How do we put it out there and know it's safe in production, we've done the right training, we've made our model resilient to what's out there? One of the things we see happening, and I love AI, love it. It has great potential, and we get great insights out of it, but a lot of times we stop, we get the insights, and we say, "Okay, it's in the box, we got a couple hits there, "we're good, it's good." No, maybe not. And so really, it's learning and creating the actually applied attacks on AI, and then figuring out what the right defenses are. And, depending on what type of machine learning you're using, those defenses change. And so, we're having a great time in our lab in Washington D.C., working on basically defending AI and building those techniques, so that what we put out as Accenture is robust. >> You know, it's interesting, AI, you watch some of the hardcore, you know, social justice warriors out there going after Amazon, Google, you know, because they're doing some pretty progressive things. Oh, facial recognition, you got AI, you got Alexa. You know, a lot of people are like, "Oh, I'm scared." But, at the end of the day, they also have some challenges like network security, so you have all this AI up and down the stack. And, one thing I like about what's being talked about in the industry is the shared responsibility model. So, I got to ask you, as AI becomes exciting, but also, balancing, frightening to people, how do you get that shared responsibility model, so we get it right, do the experimentation, without people freaking out? (laughs) So, it's kind of like this weird mode we're in now, where I want to do more AI, because I think it benefits society, but everyone's freaking out. >> Yeah, so, in our tech vision that we just launched, The Tech Vision 2020, there's a lot of talk about value and values, which is really important when we think about AI because we can get great value out of it, but there's a values piece of it and it's how we're using it, how we're getting those insights. Because, the one thing, we have this circle, and it's between customer experience, because the companies that do customer experience well are going to excel, they're going to keep their clients, they're going to do amazing things, they're going to become sticky. But, to do that well, you have to be a good custodian of their data and their information, and curated experiences that they want, and not the creepy ones, not the ones they don't want. And so, we really look at that trust is necessary in that ecosystem, in building that, and keeping that with clients. So, that's something that came out of our technology vision. And, in fact, we're going to be talking at the Executive Women's Forum, this is tomorrow, and we're going to be having a panel on AI, and defending it, which will be very interesting. >> Make sure your people film that conference. We'd like to get a view of it on YouTube after. We love those conferences, really insightful. But, I want to get back to what you were talking about, the fun side. >> Yeah. >> You got a lot of new things on, your guys are kicking the tires on, scratching the surface on. You have two operating labs, one in Washington D.C., and one in Israel. What city in Israel? Is it in Tel Aviv or-- >> Herzliya. >> Okay, did not know. >> Yeah, the tech district, just north of Tel Aviv. It's the hotspot. >> So, Silicon Valley, D.C., and Israel, hotbeds of technology now. >> Yes. >> What's coming out of those labs, what's hot? >> Oh, there's so much exciting stuff coming out of our lab in Herzliya. One of the things that we have, and it's something that's been long and coming, it's been brewing for a while, but it's really looking at creating a model of the enterprise security posture. And, when I say a model of it, I'm talking about a cyber digital twin. Because, so much we can't do in our production networks, we don't have the capabilities. We can look around the room, but we don't have the capabilities on the SOCs team side, to ingest all this stuff. We need a playground where we can ask the what-ifs, where we can run high performance analytics, and we do that through a temporal knowledge graph. And, that's a hard thing to achieve, and it's a hard thing to do analytics at scale. So, that's one of the big projects that we're doing out of our Israel lab. >> Are you saying digital twins is a framework for that? >> Yeah. >> Does it really work well with that? >> So the knowledge graph, we can create digital twins around many things, because a digital twin is a model of processes, people, technologies, the statefulness of things, and configurations, whatever you want to pull in there. So, when we start thinking about, what would we take in to create the perfect enterprise security posture? What would give us all the insights? And, then we can ask the questions about, okay, how would an adversary do lateral movement through this? I can't fix everything that's a 10, but I could fix the right ones to reduce the risk impactfully. And, those are the kind of what-ifs that you can do. >> That's real sci-fi stuff, that's right around the corner. >> Yeah, it is. >> That simulation environment. >> It is. >> What-ifs. Oh my god, the company just got hacked, we're out of business. That's your simulation. You could get to, that's the goal, right? >> It absolutely is, to ask those good business questions about the data, and then to report on the risk of it. And, the other thing, as we move to 5G, this problem's getting bigger and bigger, and we're now bringing in very disparate kinds of compute platforms, computing-at-the-edge. And, what does that do to our nice little network model that we had, that our traditional systems are used to defending against? >> I mean, just the segmentation of the network, and the edge opens up so much more aperture-- >> Yes, it does (laughs). >> to the digital twin, or a knowledge graph. You brought up knowledge graph, I want to get your thoughts on this. I was just having dinner last night with an amazing woman out of New York. She's a Ph.D. in computer science. So, we're talking about graphs, and I love riffing on graph databases. But, the topic came up about databases in general, because with the cloud, it's horizontally scalable, you've got all kinds of simulation, a lot of elasticity going on, there's a lot of software being written on this. You got time series database, you got relational database, you got unstructured, and you got graphs. You got to make them all work together. This is kind of the unique challenge. And, with security, leveraging the right database, and the right construct is a super important thing. How do you guys look at that in the labs? Because, is it something that you guys think about, or is it going to be invisible someday? >> Oh, we think about it a lot. In fact, we've had a number of research projects over the last five years now, actually six years, where we've really pivoted hard in cyber security to graph databases. And, the reason for that is, the many-to-many relationships, and what we can do in terms of navigating, asking the questions, pulling on a thread, because in cyber hunting, that's what we're doing. In many of these use cases that we're trying to defend an enterprise, we're following the next new path based on the newest information of now what the challenge is, or what the current configuration is. So, that's really important. So, graph databases enable that so well. Now, there's still the architecture challenge of, okay, when I ask a query, what am I doing? Am I disrupting the whole apple cart? Do I have to process everything over, or is there a way to do that elegantly, where I can ask my query, and because of how I've structured it in storage, I can do it much better, and I can do it much more efficiently. And that, I think, is where the opportunities are. >> I got to tell you, I'm getting exited now on this whole database discussion, because you think about the logic around what you just said. A graph database with that kind of complexity, when you factor in contextually different things happening at any given time, the database needs to be parsed and managed differently. >> Yes. >> That's a huge challenge. >> It is a great research challenge, which is why we're doing it. >> What is that, how far along are we going to be able to have this dynamic, self-evolving, self-governing, self-healing data modeling? Is that coming soon, or... >> Yeah, I hope so. We wrote about it a couple of years ago. >> You did? >> The self-healing enterprise, aspirational. But I think, I mean, we try to get to real time, right? And, we try to get to real time, and again, refactoring. As we talk about what an adversary is going to do, or lateral movement through a business process, we're talking about a lot of computational horsepower to recalculate all that, process it again, update it, and then again present that back. So the number of things we're asking, how we're asking it becomes also very important to the structure. >> Just, it goes zooming up a little bit, high level, what we're really talking about here is value >> of the data. >> Absolutely. >> And, when you get into the valuation of the nodes, and the arcs, and all that graphs, and other databases, you got to know what to pay attention to. It's kind of like going into the hospital and hearing all these alarms going off. At some point you don't know what's, until they hear a flat line, or whatever. >> Right. That's a bad one. >> I mean, well that's obvious. But, now sometimes there's so many alerts, there's so many alarms. How do you understand at any given time what to pay attention to, because obviously when someone's having a problem you want to pay attention to it. If it's a security alert, that's prioritized. >> And the devil is in the analytics, right? What's the question we're asking, and the analytics that give us that prioritization? And that's non-trivial, because there are a lot of other folks that are doing prioritization in a different manner. To do it at scale, and to do it, not just one hop out, but I want to go all the way to the crown jewels, I want that whole path navigated, and I want to know where to cut along that path. That's a hard thing to do. And so, we've actually developed, and we've submitted patents for them, but we've developed new analytics that'll support that. >> Awesome. Well Lisa, I want to ask you kind of a, I'll give you a plug here, just going to get it out, because I think it's important. Skills gap's a big thing, so I want to give you a minute to explain, or share what you're looking for in your hiring. Who are you looking for? What kind of, the make-up of individual, obviously? Maybe, do you use straight, more academic paper kind of people, or practitioners? I mean, when you look to hire, what are some of the priorities that you look for, and who would thrive in an Accenture Lab's environment? >> Oh, my goodness. >> Take a minute to share what you're looking for. >> Yeah, so we love people that think out of the box, and those kinds of people come from very different backgrounds. And so, part of that is, some of them we look for Ph.D.'s, that have wonderful applied skills, and applied is a key word there. White papers are great, I need to be able to prove something, I need to be able to demo something that has value. So, having the applied skills to a business challenge is really important. So, that sort of ground, understanding the business, very important too. But, our talent comes from many different areas. I mean, I kind of joke, my lab looks like the UN, it's wonderful. I have people from across the globe that are in our cyber security lab. I have, in our Washington D.C. lab, we're 50% women, which is also exciting, because we want different experiences, and we shoot for cognitive diversity, right? So, we're looking for people that think differently about solving problems, and are not encumbered by what they've seen in the past, because we're trying to be tip of spear. And, I'm sure you know that from Paul Daugherty. >> Yeah. >> We are trying to be three to five years over the horizon. >> You guys got a good narrative. I always love talking to Accenture, they have a good vision. So, I got to ask you, the next logical question is, obviously, in the news, you see everyone talking about breaches, and ya know, it's not a breach if the door's open, you just walk in. They're really walking in, nothing was really breached, you're just giving it to them. >> Yeah. It's a passive invitation. >> (laughs) Hey come on in. Human error is a big part of it, but then, breach is obviously targeted, phishing, and all that good stuff. But, as those stories get told, there's a whole nother set of stories that aren't being told that are super important. So, I'd love to get your thoughts on, what are the most important stories that we should be talking about that aren't being talked about? >> Yeah, so I have two that are front-of-mind for me. One theme we come back to, and it's not sexy, it's hygiene. It is IT hygiene, and so many of the large companies, and even medium, small companies, we have legacy technology, and keeping that adds complexity, it adds to the whole breadth and depth of what we have to manage and defend. Keeping that attack surface simple and small, cloud-enabled, all those good things, is a real asset and it makes it much easier to defend. So, that's kind of the first non-sexy one, hygiene. The other one I'll say that I think is a challenge that we are not dealing with yet, quantum computing, right? And so, we're on the way to getting our post quantum cryptography in place, but there's another dimension to it, and it's our histories. So, all of the things that have passed on the wire, all the communications with the key exchanges, all that brilliant stuff, is sitting somewhere. Once we get to that point where this becomes very routine, and it's coming fast, we predicted eight years, two years ago. >> So, all that exhaust is somewhere, pent up. >> It's somewhere that, we have to think about how much data we're keeping as custodians, how we're managing it, and then we have to think about the exposure from our past, and say, "Okay, what does that mean that, that was out there?" "Is it aged enough that it doesn't have value?" And, I think there's a real triage that needs to be done, and certainly data management. >> I think, you know, the hygiene brings up a good point. It reminds me of the story Andy Jassy was telling about the mainframe customer that they couldn't find who had the password. They had to find their person, who was retired 10 years earlier to get the password. You don't forget things, but also, there's a human component in all this. Humans and machines are working together. >> Absolutely. >> And. that's a huge part of it. It's not just machines dominating it all, there's going to be a human component, there's a societal impact that we're seeing with information. And, whether that's out in the open, or behind closed doors, there's all kinds of things looming. >> There are, and I think one of the things in the companies that we're seeing who are embracing innovation well, are doing a lot of retraining. Because, the things that people are excellent at, AI is not good at, and the things that AI is good at, are not at all what people are good at. So, the good news is there is a beautiful teaming there, if we retool the skills, or if we re-envision those roles, so that people can get into those roles, and I think that's really important, because I'd rather see AI do all the heavy lifting well, and be trustworthy, and robust and all those great things, and the people be doing the much smarter things that require a human. >> Does the process serve the purpose? Does the purpose serve the process? Same kind of question, right? >> Exactly. >> AI, you can't have great AI that does nothing. >> That's right. >> (laughs) So, it has to be relevant. >> It absolutely does. >> Relevance is kind of a big thing. >> And we own that context, right? Humans own that context. >> Yeah. Yeah. Yeah. Well, thanks for coming in, and sharing the insight. Really appreciate it. Final question, it's always tough to pick your favorite child, but what is your most coolest thing you're working on right now? >> I'll tell you, the cyber digital twin stuff is so cool. >> The what? >> The cyber digital twin stuff is so cool. When you see the power of what that picture, and the analytics can do, we'll show ya. >> Do you have a demo of that now? >> We absolutely do. >> You do. Is it online, or is it more in person you got to see it? >> More in person. >> Okay. >> Folks can reach out, yeah. >> We'll have to get the exclusive on that. >> We do. >> I love those simulations. I think it's very beneficial. >> It is. >> A lot of learning. I mean, who doesn't want practice? >> Well, and a picture, you know that is worth a million dollars. It's just incredible to look at it, and it clicks. It clicks of all the potential things you could ask or do. And, that's the exciting part now, as we show this with customers' and we co-innovate with customers', they're coming up with a laundry list of questions. >> And, this is the beautiful thing about cloud, is that new capabilities are emerging every day, and you could use the good ones. Lisa O'Connor is here. Thank you very much for sharing your insights. Global Security R&D Lead for Accenture Labs. TheCUBE coverage, getting all the signal here on the show floor, extracting that from all the noise. I'm John Furrier, thanks for watching. (upbeat music)