>> From Cambridge Massachusetts, it's The Cube covering MIT Chief Data Officer, and Information Quality Symposium 2019. Brought to you by SiliconANGLE Media. (electronic music) >> Welcome back to MIT in Cambridge, Massachusets everybody. You're watching The Cube, the leader in live tech coverage. My name is Dave Vellante I'm here with Paul Gillin. Day two of the of the MIT Chief Data Officer Information Quality Conference. One of the things we like to do, at these shows, we love to profile Boston area start-ups that are focused on data, and in particular we love to focus on start-ups that are founded by women. Julie Johnson is here, She's the Co-founder and CEO of Armored Things. Julie, great to see you again. Thanks for coming on. >> Great to see you. >> So why did you start Armored Things? >> You know, Armored Things was created around a mission to keep people safe. Early in the time where were looking at starting this company, incidents like Las Vegas happened, Parkland happened, and we realized that the world of security and operations was really stuck in the past right? It's a manual solutions generally driven by a human instinct, anecdotal evidence, and tools like Walkie-Talkies and video cameras. We knew there had to be a better way right? In the world of Data that we live in today, I would ask if either of you got in your car this morning without turning on Google Maps to see where you were going, and the best route with traffic. We want to help universities, ball parks, corporate campuses do that for people. How do we keep our people safe? By understanding how they live. >> Yeah, and stay away from Lambert Street in Cambridge by the way. >> (laughing) >> Okay so, you know in people, when they think about security they think about cyber, they think about virtual security, et cetera et cetera, but there's also the physical security aspect. Can you talk about the balance of those two? >> Yeah, and I think both are very important. We actually tend to mimic some of the revolutions that have happened on the cyber security side over the last 10 years with what we're trying to do in the world of physical security. So, folks watching this who are familiar with cyber security might understand concepts like anomaly detection, SIEM and SOAR for orchestrated response. We very much believe that similar concepts can be applied to the physical world, but the unique thing about the physical world, is that it has defined boundaries, right? People behave in accordance with their environment. So, how do we take the lessons learned in cyber security over 10 to 15 years, and apply them to that physical world? I also believe that physical and cyber security are converging. So, are there things that we know in the physical world because of how we approach the problem? That can be a leading indicator of a threat in either the physical world or the digital world. What many people don't understand is that for some of these cyber security hacks, the first weak link is physical access to your network, to your data, to your systems. How do we actually help you get an eye on that, so you already have some context when you notice it in the digital realm. >> So, go back to the two examples you sited earlier, the two shooting examples. Could those have been prevented or mitigated in some way using the type of technology you're building? >> Yeah, I hate to say that you could ever prevent an incident like that. Everyone wants us to do better. Our goal is to get a better sense predicatively of the leading indicators that tell you you have a problem. So, because we're fundamentally looking at patterns of people and flow, I want to know when a normal random environment starts to disperse in a certain way, or if I have a bottle neck in my environment. Because if then I have that type of incident occur, I already know where my hotspots are, where my pockets of risk are. So, I can address it that much more efficiently from a response perspective. >> So if people are moving quickly away from a venue, it might be and indication that there's something wrong- >> It could be, Yeah. That demands attention. >> Yeah, when you go to a baseball game, or when you go to work I would imagine that you generally have a certain pattern of behavior. People know conceptually what those patterns are. But, we're the first effort to bring them data to prove what those patterns are so that they can actually use that data to consistently re-examine their operations, re-examine their security from a staffing perspective, from a management perspective, to make sure that they're using all the data that's at their disposal. >> Seems like there would be many other applications beyond security of this type of analysis. Are you committed to the security space, or do you have broader ambitions? >> Are we committed to the security space is a hundred percent. I would say the number one reason why people join our team, and the number one reason why people call us to be customers is for security. There's a better way to do things. We fundamentally believe that every ball park, every university, every corporate campus, needs a better way. I think what we've seen though is exactly what you're saying. As we built our software, for security in these venues, and started with an understanding of people and flow, there's a lot that falls out of that right? How do I open gates that are more effective based on patterns of entry and exit. How do I make sure that my staffing's appropriate for the number of people I have in my environment. There's lots of other contextual information that can ultimately drive a bottom line or top line revenue. So, you take a pro sports venue for example. If we know that on a 10 degree colder day people tend to eagres more early in the game, how do we adjust our food and beverage strategy to save money on hourly workers, so that we're not over staffing in a period of time that doesn't need those resources. >> She's talking about the physical and the logical security worlds coming together, and security of course has always been about data, but 10 years ago it was staring at logs increasing the machines are helping us do that, and software is helping us do that. So can you add some color to at least the trends in the market generally, and then maybe specifically what you're doing bringing machine intelligence to the data to make us more secure. >> Sure, and I hate to break it to you, but logs are still a pretty big part of what people are watching on a daily basis, as are video cameras. We've seen a lot of great technology evolve in the video management system realm. Very advanced technology great at object recognition and detecting certain behaviors with a video only solution, right? How do we help pinpoint certain behaviors on a specific frame or specific camera. The only problem with that is, if you have people watching those cameras, you're still relying on humans in the loop to catch a malicious behavior, to respond in the event that they're notified about something unusual. That still becomes a manual process. What we do, is we use data to watch not only cameras, but we are watching your cameras, your Wi-Fi, access control. Contextual data from public transit, or weather. How do we get this greater understanding of your environment that helps us watch everything so that we can surface the things that you want the humans in the loop to pay attention to, right? So, we're not trying to remove the human, we're trying to help them focus their time and make decisions that are backed by data in the most efficient way possible. >> How about the concerns about The Surveillance Society? In some countries, it's just taken for granted now that you're on camera all the time. In the US that's a little bit more controversial. Is what your doing, do you have to be sensitive to that in designing the tools you're building? >> Yeah, and I think to Dave's question, there are solutions like facial recognition which are very much working on identifying the individual. We have a philosophy as a company, that security doesn't necessarily start with the individual, it starts with the aggregate. How do we understand at an aggregate macro level, the patterns in an environment. Which means I don't have to identify Paul, or I don't have to identify Dave. I want to look for what's usual and unusual, and use that as the basis of my response. There's certain instances where you want to know who people are. Do I want to know who my security personnel are so I can dispatch them more efficiently? Absolutely. Let's opt those people in and allow them to share the information they need to share to be better resources for our environment. But, that's the exception not the norm. If we make the norm privacy first, I think we'll be really successful in this emerging GDPR data centric world. >> But I could see somebody down the road saying hey can you help us find this bad guy? And my kids at camp this week, This is his 7th year of camp, and this year was the first year my wife, she was able to sign up for a facial recognition thing. So, we used to have to scroll through hundreds and hundreds of pictures to see oh, there he is! And so Deb signs up for this thing, and then it pings you when your son has a picture taken. >> Yeah. And I was like, That's awesome. Oh. (laughing) >> That's great until you think about it. >> But there aren't really any clear privacy laws today. And so you guys are saying, look it, we're looking at the big picture. >> That's right. >> But that day is coming isn't it? >> There's certain environments that care more than others. If you think about universities, which is where we first started building our technology, they cared greatly about the privacy of their students. Health care is a great example. We want to make sure that we're protecting peoples personal data at a different level. Not only because that's the right thing to do, but also from a regulatory perspective. So, how do we give them the same security without compromising the privacy. >> Talk about Bottom line. You mentioned to us earlier that you just signed a contract with a sports franchise, you're actually going to help them, help save them money by deploying their resources more efficiently. How does your technology help the bottom line? >> Sure, you're average sporting venue, is getting great information at the point a ticket is scanned or a ticket is purchased, they have very little visibility beyond that into the customer journey during an event at their venue. So, if you think about again, patterns of people and flow from a security perspective, at our core we're helping them staff the right gates, or figure out where people need to be based on hot spots in their environment. But, what that also results in is an ability to drive other operational benefits. Do we have a zone that's very low utilization that we could use as maybe even a benefit to our avid fans. Send them to that area, get traffic in that area, and now give them a better concession experience because of it, right? Where they're going to end up spending more money because they're not waiting in line in the different zone. So, how do we give them a dashboard in real time, but also alerts or reports that they can use on an ongoing basis to change their decision making going forward. >> So, give us the company overview. Where are you guys at with funding, head count, all that good stuff. >> So, we raised a seed round with some great Boston and Silicon Valley investors a year ago. So, that was Glasswing is a Boston AI focused fund, has been a great partner for us, and Inovia which is Canada's largest VC fund recently opened a Silicon Valley office. We just started raising a series A about a week ago. I'm excited to say those conversation have been going really well so far. We have some potential strategic partners who we're excited about who know data better then anyone else that we think would help us accelerate our business. We also have a few folks who are very familiar with the large venue space. You know, the distributed campuses, the sporting and entertainment venues. So, we're out looking for the right partner to lead our series A round, and take our business to the next level, but where we are today with five really great branded customers, I think we'll have 20 by the end of next year, and we won't stop fighting 'till we're at every ball park, every football stadium, every convention center, school. >> The big question, at some point will you be able to eliminate security lines? (laughing) >> I don't think that's my core mission. (laughing) But, optimistically I'd love to help you. Right, I think there's some very talented people working on that challenge, so I'll defer that one to them. >> And rough head count today? >> We have 23 people. >> You're 23 people so- >> Yeah, I headquartered in Boston Post Office Square. >> Awesome, great location. So, and you say you've got five customers, so you're generating revenue? >> Yes >> Okay, good. Well, thank you for coming in The Cube >> Yeah, thank you. >> And best of luck with the series A- >> I appreciate it and going forward >> Yeah, great. >> All right, and thank you for watching. Paul Gillin and I will be back right after this short break. This is The Cube from MIT Chief Data Officer Information Quality Conference in Cambridge. We'll be right back. (electronic music)

(pulsating electronic music) >> Welcome everybody, this is theCube, I'm Paul Gillin. Physical security and cybersecurity have traditionally been sort of isolated worlds, they didn't talk to each other. But in the age of the Internet of Things we now have unprecedented opportunities to unite these two traditionally separate areas. Armored Things is a startup out of Boston and is doing some very interesting work in using intelligent devices to make decisions and to intuit patterns in crowd behavior which has applications in cybersecurity, crowd management, traffic management, a lot of different potential uses of this technology. With me are Julie Johnson the co-founder and President of Armored Things, and Chris Lord, the Chief Technology Officer, Welcome. >> Thank you. >> Why don't you describe in a nutshell, let's start out, what you do Julie. >> Great, Armored things is building software to do next generation incident response. We're using the IOT devices and their data to power decisions across large environments used for safety. So for example the data that we're collecting can be used to get better situational awareness within seconds and drive incident response in seconds instead tens of minutes, which is the state of the art today. >> And so it's sounds like, is security the primary target area or are there others? >> That's right, we sit at the intersection of physical and cybersecurity. This information can also be used to drive additional value over time but right now we're really focused on achieving that mission, using these devices, this technology to improve both the physical and cyber realms for Internet of Things. >> Chris why don't you give us an example of how your technology might be applied? >> Sure, so a very common one is, you know active shooter. People are very concerned about active shooter, and so how can you leverage all the data that you have across different devices, different systems that you have out there, in order to understand what happened, and get people the right information at the right time. A more commonplace example might be something like a protest formation. So if you look at a university campus where you might have a controversial group meeting on campus and you need to get early warning when there's a protest forming on the other side. Our technology will allow you to see that before it's gotten to a critical proportion or before it's marching down the street. >> So why don't you take a deeper dive and talk about what, how are you federating these devices? How are you using these multiple devices together? >> Well that's exactly what we are. So we're a data analytics layer across all the silos of data that you already have in your environment. So as you look around you might have motion sensors in your environment, you might have access control systems in your environment, you have wireless infrastructure in your environment, all these things are used for specific purposes now but nothings really trying to correlate and connect the data across all of them. So Armored Things builds a layer across all of them, brings that data together to give you better understanding of what's going on in your environment, people and your physical space. >> Julie talk about how the company came about, what are the origins? >> Sure, so I started working with Charles Curran our CEO about two years ago at Qualcomm. We were really focused on understanding the security portion of the IOT layer and how to manage these things in enterprise. So if you're familiar with IOT in the household there's been a lot of proliferation around turning your lights on, understanding who's at your front door, but in enterprise it's been much slower to adopt. Fundamentally we believe that part of that was because management took a lot of time. Every time you provisioned a device it took a number of minutes and because there was an intrinsic lack of security on each of the devices. So we went around and started talking to different potential customer groups about what it would look like to bring more IOT into their environments. And we really got pulled into universities, and large sporting and entertainment venues, who we're still working with as our primary customers today. Because they saw a desperate need for IOT, not only to save time on managing these devices, and to make sure that they're secure in their environments, but also to use them for physical security. So now that we've spent, you know $15 million in selling IP video cameras, or a few million dollars in selling access control systems, how do we actually elevate their use from what they were initially intended for. That spend has a secondary use when it comes to physical security. That ability to, you know quickly get cameras on the scene of an incident. That ability to harness data coming off of motion sensors or environmental sensors. How do we use all of that information to drive an awareness of our environments day-to-day and then use it in critical emergencies for a better response. >> I understand you're working with some sports teams right now. Can you describe a scenario in which you might be able to help them manage crowds more effectively? >> So there was a great example we heard about two weeks ago from a top team, who's recently hosted some World Series events. They had a unfortunate incident where they were watching, they were hosting a watch party for the World Series in their venue during an away game, and they handed about 40,000 paper tickets out. They got a great turnout, 20,000 people came to the venue. But in the seventh inning of the game the other 20,000 people decided that they also wanted to be in the venue in order to celebrate. That was a pretty unanticipated event. Usually in the fifth or sixth inning you start to consolidate your entrances, you start to consolidate your security personnel and send them to other parts of the venue, and the net result of that was they ended up closing the doors, not allowing additional entrance in, and tweeting that there wouldn't be additional people allowed to enter. There were a lot of security issues with letting 20,000 people in, in the seventh inning, not of the least is you don't know where they're coming from, and you don't really know what their intent is in coming so late to that venue. But there's patterns in the data that we could've seen sooner. So hypothetically, understanding that a normal game day has a couple hundred people entering in the fifth, sixth, seventh innings. Seeing a significant uptick in that number of people coming into your environment should immediately say, what's unique, you know what's different about this situation? Now how do I tie in my resources, my security personnel, my responders, and just maybe notify people who are in charge of making these types of decisions, so that we're not closing the gate and tweeting out to our fans that there's no more entries. >> And getting back to the technical nuances of this situation, how might your technology detect this crowd assembling before it was even visually apparent? >> Good question, so there's many, many different things. So part of what we do is rely on diversity of data from different sources. So that might be mobile devices. That might be from wireless. That might be from cameras that you have there and doing occupancy counts on those cameras. It might be from other, you know motion sensors you have in your environment. All this data gets aggregated so that we can come up with a good understanding of population and flow within your environment. So we would have early indications and bring that awareness to people that have to respond, people who might be sitting in a network operations center, and looking at other cameras but not seeing the information. So we can bring the information right there, notify them that there's a problem forming before it's gotten to critical proportions. >> Fantastic. >> One more thought on that is there's kind of a unique advantage in data to go beyond what humans can perceive. When we're looking at these knocks, you know they have thousands of video cameras potentially united in one central screen. It takes not only having the right camera up but also noticing a degree of difference that might be quite minute, to actually see it as an anomaly in real-time. So you can imagine, you know a university campus where people are walking through the campus at a certain pace every single day. One day everyone's walking just 30% faster, not running just walking, why? You know is there a suspicious package? Is there someone gathered there that you know is attracting people that they don't necessarily want to be associated with, or end up in a vulnerable position? How can we see that in the data faster than someone in the control room might notice it and alert people to respond. >> And with machine learning, of course now we have the means to do that. Chris, talk about the, it strikes me that there must be a lot of complexity involved. You've got a great diversity of devices out there you have to connect to. Every institution would have a different fabric. How are you technically pulling this all together? >> Well the nice thing about a lot of these technologies is there is standardization across many of these different types of devices, and there are, you know there are tiers of players right. And so we do have to be selective about who we integrate with. We are integrated with the top-tier players in all these categories, and we'll prioritize other integrations over time based on our customers and our market so. >> And Julie, what are your plans for deployment? What's your timeframe? >> We're looking to rollout our first generation of product in the next nine to twelve months. That really drives home at that situational awareness piece. So before we even get to building through incident response at scale, the ability to give people very specific cues during a critical emergency. How do we start with getting more information to the people who are there? So getting occupancy, flow, the dynamics of movement around a campus or a large venue. How do we start equipping the police personnel, and security personnel to make better decisions and drive value from there. >> I understand there's no shortage of demand for your solution. >> We do have some top-tier universities, and pro-sporting and entertainment venues who we're working with to build the right solution not just the solution that we think is needed, but the solution that they're telling us, "Hey we would really like to use something like this." >> I also understand you've pulled together a team, kind of a dream team, talk about some of the people that you've brought on board for this operation which few people have even heard of. >> Yeah so I think the first of those you're seeing here, so Chris joined us as co-founder and CTO and has been really an asset to this team given his background in cybersecurity from Carbon Black and before that. And you know if you want to add more to that please feel free to. >> No thanks. >> We've also brought in, I would call it two pillars of our strategy. One one the physical security side and one on the machine learning data analytics side, and those two women are Elizabeth Carter. Who came to us from Apple, where she led crisis management for the Americas. She previously worked at Chertoff Group where she sat at the intersection of physical and cybersecurity, and before that actually worked for the city of New York, where she understood weapons of mass destruction, different types of biological and chemical weapons response planning. So she's kind of the pillar of our physical security response understanding and driving product. The other woman, her name is Clare Bernard and she recently joined us from another Boston startup called Tamr where she was running product and engineering for them. Clare's background is actually in particle physics. She was BU and John's Hopkins, and happened to work with the team that discovered the God particle while she was getting her PhD. So we' think she's as smart as you can find, and is going to help us think about these data challenges, the analytics piece at a scale that, you know we think has the potential to really improve physical security and cybersecurity. I would be remiss if I didn't mention the rest of our team. Our CEO Charles comes from a background in the venture capital community and is just incredibly knowledgeable about the process of building a company from the ground up, and has many skills when it comes to recruiting as well. Really helped drive some of these hires forward and the rest of the team is the next generation of rising stars, people from Oracle, HP Vertica, other Carbon Black individuals. People who just have experience from across the board that's going to help us build the right solution. >> And you know at a time when diversity has been a major issue for tech companies, I understand your team is unusually well represented. >> I think our executive team is about 60% women, which we're very proud of. I think our team in general might actually be, >> About that too, yup. >> About 60% women, which we're also very proud of. And I'd like to say that that's organic. That we've worked with some great advisors and potential customers, and I do think that from my perspective, it's been helpful to have younger women coming in who see a path forward for senior women in executive roles in their company. I think that's something that can't be underestimated. >> Where do you stand in funding right now? >> We just closed our first institutional capital about a week and a half ago. We're still finishing the close of that round but we have a Boston based partner who's very focused on machine learning and analytics, and also has been a well recognized investor in the cyber security realm. So we're very fortunate to have this investor as our partner, and excited to keep working with them. >> Chris, as someone whose background is in cybersecurity how do you see the security landscape changing now with the IOT coming on and the possibility of really transforming the way organizations look at their physical and cybersecurity operations? >> Good question, so over time they're converging, and they're converging I think more rapidly than we expected, so now I'm going to step back a little bit and say that there's a lot of parallels. Cybersecurity I think is probably about five years ahead of physical security in terms of maturity of technology and approaches to problems. And then so what we're seeing right now, and we're part of the force behind that, is taking the learnings from cyber security and applying them to physical security right. So when we talk about situational awareness, when we talk about the data analytics that supports that, and when we talk about incident response and orchestration automation. All of those are core to taking cybersecurity and applying it to physical security. In terms of convergence, we're seeing many cases, and this is going back a number of years, where people are using cyber events to create physical problems right. Stuxnet is a classic example, but you can do the same thing by taking over something and instilling panic in a stadium, and causing you know, all sorts of grief, cyber driving physical. You can also see cases where people who are running cybersecurity operation centers want access to physical knowledge of their environment in order to do their job better. Whether it is a malicious insider that they suspect, whether it's an infection that occurs on a particular machine, being able to pull up the cameras, know who was there at the time, bringing all that information together, is again necessary in order to understand their perception of situational awareness. So two converging towards one, we're going to be building towards that goal from our perspective. >> Now the flip side of federating IOT devices is that the bad guys can do the same thing. So you potentially have a much broader attack surface. That has to be factoring into your thinking. What is the embedded security in your platform? >> So, we're not going to address fully that right now, but so we take advantage of best in breed security principles in our design both for security and for privacy. But in terms of the dependency we have on a lot of IOT devices and IOT systems, part of what helps us is diversity of data across those, and diversity of devices right. And so while you might have compromises in specific cases, the fact that you are dealing with so many, and so many different categories at the same time, allows you to maintain and fulfill your mission, and deliver what you're trying to do regardless of some of those individual compromises. We're also in a unique vantage point where we can actually see the operational integrity of what's going on. So when you look across all those different categories and you look at the data that we're collecting, whether it's malicious or not, we're able to identify a failure, and bring that to the attention of the people who are dependent on those systems. So we could be an early morning to cyber events, malicious or not. >> Julie, entrepreneurs love to dream. I'm sure you are thinking big, beyond the immediate cybersecurity applications. Where could Armored Things eventually go? >> That's a great question. The dream is that we become not only the dominant solution for physical and cyber security for schools and large venues. But we bring our solution into K, 12 where some of this is desperately needed. That's kind of the mission orientation of our team. How do we start to drive value in a way that we can get to every school in the country sooner. In the longer term though, I think there's a lot of opportunities with IOT and we're still kind of at the tip of the iceberg here. We're going to see all sorts of new devices come online over the next two, five, 10 years. The growth of these devices is incredible. And the question is how do we continue this challenge of solving the data at scale in a way that continues to drive value, not just for some of the first use cases, which are often around marketing, and understanding an environment in that sense, but also continuing that physical cybersecurity angle. >> Enormous potential and hope you stay based in Boston. We can use more companies like that. Chris Lord and Julie Johnson, thanks very much for joining us today on theCUbe. >> Thanks Paul. >> Thank you. >> Armored Things, keep your eye on them. You're going to be hearing a lot more about this company in the months to come. I'm Paul Gillin, this is theCube.