[Music] hi I'm Peter Burris and welcome to another Cube conversation from the cube studios here in beautiful Palo Alto California today we're going to talk about some new things that are happening in the security world obviously this is one of the most important domains within the technology industry and increasingly because of digital business in business overall now to do that we've asked Eric manki to come back Derick is the chief of security insights and global threat alliances at Fort Net Derek welcome back to the cube absolutely the same feel the same way Derek okay so we're going to get into some some predictions about what the bad guys are doing and some predictions about what the defenses are doing how we're going to see them defense opportunities improve but let's set the stage because predictions always are made on some platforms some understanding of where we are and that has also changed pretty dramatically so what's the current state in the overall security world Derek yeah so what we saw this year in 2019 a lot is a big increase on automation and I'm talking from an attackers point of view I think we talked about this a little bit earlier in the year so what we've been seeing is the use of frameworks to enhance sort of the day-to-day cycles that cyber criminals and attackers are using to make their you know criminal operations is that much more efficient sort of a well-oiled machine so we're seeing toolkits that are taking you know things within the attack cycle and attack change such as reconnaissance penetration you know exploitation getting into systems and just making that that much quicker so that that window to attack the time to breach has been shrinking thanks to a lot of these crime kits and services that are offered out there now one other comment on this or another question that I might have on this is that so speed is becoming an issue but also the risk as digital business takes on a larger four portion of overall business activities that ultimately the risks and costs of doing things wrong is also going up if I got the right yeah absolutely for sure and you know it's one of those things that it's the longer that a cybercriminal has a foothold in your system or has the opportunity to move laterally and gain access to other systems maybe it's your I o T or you know other other platforms the higher the risk right like the deeper down they are within an attack cycle the higher the risk and because of these automated toolkits are allowing allowing them to facilitate that it's a catalyst really right they can get into the system they can actually get out that much quicker the risk is a much higher and we're talking about risk we're talking about things like intellectual property exfiltration client information this sort of stuff that can be quite damaging to organizations so with the new foundation of speed is becoming an increasingly important feature probably think about security and the risks are becoming greater because digital assets are being recognized as more valuable why do you take us through some of the four Donets predictions on some of the new threats or the threat landscape how's the threat landscape changing yeah so as I said we've already seen this shift in automation so what I would call the basics I mean knowing the target trying to break into that target right when it comes to breaking into the target cyber criminals right now they're following the path of least resistance right they're finding easy ways that they can get into IOT devices I into other systems in our world when we talk about penetration or breaking into systems it's through zero days right so the idea of a zero day is essentially a cyber weapon there's movies and Hollywood that have been made off of this you look at attacks like Stuxnet in the past they all use zero day vulnerabilities to get into systems all right so the idea of one of the predictions we're seeing is that cyber criminals are gonna start to use artificial intelligence right so we talk about machine learning models and artificial intelligence to actually find these zero days for them so in the world of an attacker to find a zero day they have to do a practice called fuzzing and fuzzing is basically trying to trick up computer code right so you're throwing unverified parameters out at your turn T of throwing and unanticipated sequences into code parameters and and input validation and so forth to the point that the code crashes and that's from an attackers point of view that's when you take control of that code this how you know finding weapons into system cyber weapons in this systems work it typically takes a lot of a lot of resource it takes a lot of cycles it takes a lot of intelligence that takes a lot of time to discovery we can be talking on month for longer it's one of the predictions that we're hitting on is that you know cyber criminals are gonna start to use artificial intelligence fuzzing or AI F as I call it to be able to use AI to do all of that you know intelligent work for them so you know basically having a system that will find these gateways if you will these these you know new vulnerabilities into systems so sustained use of AI F to corrupt models so that they can find vulnerabilities that can then be exploited yeah absolutely and you know when it comes to the world of hacking and fuzzing it's one of the toughest things to do it is the reason that zero days are worth so much money you know they can suffer hundreds of thousands of dollars on darknet and in the cyber criminal you know economy so it's because they're talk talk to finally take a lot of resources a lot of intelligence and a lot of effort to be able to not only find the vulnerability but then actively attack it and exploit it right there's two phases to that yeah so the idea is by using part of the power of artificial intelligence that cyber criminals will start to leverage that and harness it in a bad way to be able to not only discover you know these vulnerabilities but also create that weapon right create the exploit so that they can find more you know more holes if you will or more angles to be able to get into systems now another one is that virtualization is happening in you know what the good guys as we virtualized resources but is it also being exploited or does it have the potential be exploited by the bad guys as well especially in a swarming approach yeah virtualization for sure absolutely so the thing about virtualization too is you often have a lot of virtualization being centralizes especially when we talk about cloud right so you have a lot of potential digital assets you know valuable digital assets that could be physically located in one area so when it comes to using things like artificial intelligence fuzzing not only can it be used to find different vulnerabilities or ways into systems it can also be combined with something like I know we've talked about the const that's warm before so using you know multiple intelligence infected pieces of code that can actually try to break into other virtual resources as well so virtualization asked definitely it because of in some cases close proximity if you will between hypervisors and things like this it's also something of concern for sure now there is a difference between AI fai fuzzing and machine learning talk to us a little bit about some of the trends or some of the predictions that pertain to the advancement of machine learning and how bad guys are going to exploit that sure so machine learning is a core element that is used by artificial intelligence right if you think of artificial intelligence it's a larger term it can be used to do intelligent things but it can only make those decisions based off of a knowledge base right and that's where machine learning comes into place machine learning is it's data it's processing and it's time right so there's various machine learning learning models that are put in place it can be used from everything from autonomous vehicles to speech recognition to certainly cybersecurity and defense that we can talk about but you know the other part that we're talking about in terms of reductions is that it can be used like any tool by the bad guys so the idea is that machine learning can be used to actually study code you know from from a black hat attacker point of view to studying weaknesses in code and that's the idea of artificial intelligence fuzzing is that machine learning is used to find software flaws it finds the weak spots in code and then it actually takes those sweet spots and it starts probing starts trying to attack a crisis you know to make the code crash and then when it actually finds that it can crash the code and that it can try to take advantage of that that's where the artificial intelligence comes in right so the AI engine says hey I learned that this piece of software or this attack target has these weak pieces of code in it that's for the AI model so the I fuzzy comes into place to say how can I actually take advantage how can i exploit this right so that's where the AI trussing comes into play so we've got some predictions about how black hats and bad guys are going to use AI and related technologies to find new vulnerabilities new ways of exploiting things and interacting new types of value out of a business what are the white hats got going for them what are their some of the predictions on some of the new classes of defense that we're going to be able to put to counter some of these new classes of attacks yeah so that's that's you know that's honestly some of the good news I believe you know it's always been an armor an arms race between the bad guys and the good guys that's been going on for decades in terms of cybersecurity often you know the the bad guys are in a favorable position because they can do a million things wrong and they don't care right from the good guys standpoint we can do a million things right one thing wrong and that's an issue so we have to be extra diligent and careful with what we do but with that said you know as an example of 49 we've deployed our forty guard AI right so this is six years in the making six years using machine learning using you know precise models to get higher accuracy low false positives to deploy this at reduction so you know when it comes to the defensive mechanism I really think that we're in the drivers position quite frankly we have better technology than the Wild West that they have out on the bad guys side you know from an organization point of view how do you start combating this sort of onslaught of automation in AI from from the bad guys side well you gotta fight fire with fire right and what I mean by that is you have to have an intelligent security system you know perimeter based firewalls and gateways they don't cut it anymore right you need threat intelligence you need systems that are able to orchestrate and automate together so in different security products and in your security stack or a security fabric that can talk to each other you know share intelligence and then actually automate that so I'm talking about things like creating automated security policies based off of you know threat intelligence finding that a potential threat is trying to get into your network that sort of speed through that integration on the defensive side that intelligence speed is is is the key for it I mean without that any organization is gonna be losing the arms race and I think one of the things that is also happening is we're seeing a greater willingness perhaps not to share data but to share information about the bad things that are happening and I know that fort and it's been something at the vanguard of ensuring that there's even better clearing for this information and then driving that back into code that actually further automates how customers respond to things if I got that right yeah you hit a dead-on absolutely you know that is one of the key things that were focused on is that we realized we can't win this war alone right nobody can on a single point of view so we're doing things like interoperating with security partners we have a fabric ready program as an example we're doing a lot of work in the industry working with as an example Interpol and law enforcement to try to do attribution but though the whole endgame what we're trying to do is to the strategy is to try to make it more expensive for cyber criminals to operate so we obviously do that as a vendor you know through good technology our security fabric I integrated holistic security fabric and approach to be able to make it tougher you know for attackers to get into systems but at the same time you know we're working with law enforcement to find out who these guys are to go after attribution prosecution cut off the head of the snake as I call it right to try to hit cyber criminal organizations where it hurts we're also doing things across vendor in the industry like cyber threat Alliance so you know forty knots a founding member of the cyber threat Alliance we're working with other security vendors to actually share real time information is that speed you know message that we're talking about earlier to share real time information so that each member can take that information and put it into you something actionable right in our case when we get intelligence from other vendors in the cyber threat Alliance as an example we're putting that into our security fabric to protect our customers in new real-time so in sum we're talking about a greater value from being attacked being met with a greater and more cooperative use of technology and process to counter those attacks all right yeah absolutely so open collaboration unified collaboration is is definitely key when it comes to that as well you know the other thing like I said is is it's the is the technology piece you know having integration another thing from the defensive side too which is becoming more of a topic recently is deception deception techniques this is a fascinating area to me right because the idea of deception is the way it sounds instead of to deceive criminals when they're coming knocking on your door into your network so it's really what I call like the the house of a thousand mirrors right so they get into your network and they think they're going to your data store but is it really your data store right it's like it's there's one right target and a thousand wrong targets it's it's a it's a defensive strategy that organizations can play to try to trip up cyber criminals right it makes them slower it makes them more inaccurate it makes them go on the defensive and back to the drawing board which is something absolutely I think we have to do so it's very interesting promising you know technology moving forward in 2019 to essentially fight back against the cyber criminals and to make it more expensive to get access to whatever it is that they want Derek max Lilly yeah Derrick McKey chief of security insights and global threat Alliance this is for net thanks once again for being on the cube it's a pleasure anytime look forward to the next chat and from Peter Burroughs and all of us here at the cube in Palo Alto thank you very much for watching this cube conversation until next time you

hey welcome back everybody Jeff Rick here with the cube we're at the mill pedis at an interesting event is called the auto tech council innovation in motion mapping and navigation event so a lot of talk about autonomous vehicles so it's a lot of elements to autonomous vehicles this is just one small piece of it it's about mapping and navigation and we're excited to have with us our first guest again and give us a background of this whole situation just Derick Curtin and he's the founder and chairman of the auto tech council so first up there welcome thank you very much good to be here absolutely so for the folks that aren't familiar what is the auto tech council autofit council is a sort of a club based in Silicon Valley where we have gathered together some of the industry's largest OMS om is mean car makers you know of like Rio de Gono from France and a variety of other ones they have offices here in Silicon Valley right and their job is to find innovation you find that Silicon Valley spark and take it back and get it into cars eventually and so what we are able to do is gather them up put them in a club and route a whole bunch of Silicon Valley startups and startups from other places to in front of them in a sort of parade and say these are some of the interesting technologies of the month so did they reach out for you did you see an opportunity because obviously they've all got the the Innovation Centers here we were at the Ford launch of their innovation center you see that the tagline is all around is there too now Palo Alto and up and down the peninsula so you know they're all here so was this something that they really needed an assist with that something opportunity saw or was it did it come from more the technology side to say we needed I have a new one to go talk to Raja Ford's well it's certainly true that they came on their own so they spotted Silicon Valley said this is now relevant to us where historically we were able to do our own R&D build our stuff in Detroit or in Japan or whatever the cases all of a sudden these Silicon Valley technologies are increasingly relevant to us and in fact disruptive to us we better get our finger on that pulse and they came here of their own at the time we were already running something called the telecom Council Silicon Valley where we're doing a similar thing for phone companies here so we had a structure in place that we needed to translate that into beyond modem industry and meet all those guys and say listen we can help you we're going to be a great tool in your toolkit to work the valley ok and then specifically what types of activities do you do with them to execute division you know it's interesting when we launched this about five years ago we're thinking well we have telecommunication back when we don't have the automotive skills but we have the organizational skills what turned out to be the cases they're not coming here the car bakers and the tier 1 vendors that sell to them they're not coming here to study break pad material science and things like that they're coming to Silicon Valley to find the same stuff the phone company two years ago it's lookin at least of you know how does Facebook work in a car out of all these sensors that we have in phones relate to automotive industry accelerometers are now much cheaper because of reaching economies of scale and phones so how do we use those more effectively hey GPS is you know reach scale economies how do we put more GPS in cars how do we provide mapping solutions all these things you'll set you'll see and sound very familiar right from that smartphone industry in fact the thing that disrupts them the thing that they're here for that brought them here and out of out of defensive need to be here is the fact that the smartphone itself was that disruptive factor inside the car right right so you have events like today so gives little story what's it today a today's event is called the mapping and navigation event what are people who are not here what's what's happening well so every now and then we pick a theme that's really relevant or interesting so today is mapping and navigation actually specifically today is high definition mapping and sensors and so there's been a battle in the automotive industry for the autonomous driving space hey what will control an autonomous car will it be using a map that's stored in memory onboard the car it knows what the world looked like when they mapped it six months ago say and it follows along a pre-programmed route inside of that world a 3d model world or is it a car more likely with the Tesla's current they're doing where it has a range of sensors on it and the sensors don't know anything about the world around the corner they only know what they're sensing right around them and they drive within that environment so there's two competing ways of modeling a 3d world around autonomous car and I think you know there was a battle looking backwards which one is going to win and I think the industry has come to terms with the fact the answer is both more everyday and so today we're talking about both and how to infuse those two and make better self-driving vehicles so for the outsider looking in right I'm sure they get wait the mapping wars are over you know Google Maps what else is there right but then I see we've got TomTom and meet a bunch of names that we've seen you know kind of pre pre Google Maps and you know shame on me I said the same thing when Google came out with a cert I'm like certain doors are over who's good with so so do well so Eddie's interesting there's a lot of different angles to this beyond just the Google map that you get on your phone well anything MapQuest what do you hear you moved on from MapQuest you print it out you're good together right well that's my little friends okay yeah some people written about some we're burning through paper listen the the upshot is that you've MapQuest is an interesting starting board probably first it's these maps folding maps we have in our car there's a best thing we have then we move to MapQuest era and $5,000 Sat Navs in some cars and then you might jump forward to where Google had kind of dominate they offered it for free kicked you know that was the disruptive factor one of the things where people use their smartphones in the car instead of paying $5,000 like car sat-nav and that was a long-running error that we have in very recent memory but the fact of the matter is when you talk about self-driving cars or autonomous vehicles now you need a much higher level of detail than TURN RIGHT in 400 feet right that's that's great for a human who's driving the car but for a computer driving the car you need to know turn right in 400.000 five feet and adjust one quarter inch to the left please so the level of detail requires much higher and so companies like TomTom like a variety of them that are making more high-level Maps Nokia's form a company called here is doing a good job and now a class of car makers lots of startups and there's crowdsource mapping out there as well and the idea is how do we get incredibly granular high detail maps that we can push into a car so that it has that reference of a 3d world that is extremely accurate and then the next problem is oh how do we keep those things up to date because when we Matt when when a car from this a Nokia here here's the company house drives down the street does a very high-level resolution map with all the equipment you see on some of these cars except for there was a construction zone when they mapped it and the construction zone is now gone right update these things so these are very important questions if you want to have to get the answers correct and in the car stored well for that credit self drive and once again we get back to something to mention just two minutes ago the answer is sensor fusion it's a map as a mix of high-level maps you've got in the car and what the sensors are telling you in real time so the sensors are now being used for what's going on right now and the maps are give me a high level of detail from six months ago and when this road was driven it's interesting back of the day right when we had to have the CD for your own board mapping Houston we had to keep that thing updated and you could actually get to the edge of the sea didn't work we were in the islands are they covering here too which feeds into this is kind of of the optical sensors because there's kind of the light our school of thought and then there's the the biopic cameras tripod and again the answers probably both yeah well good that's a you know that's there's all these beat little battles shaping up in the industry and that's one of them for sure which is lidar versus everything else lidar is the gold standard for building I keep saying a 3d model and that's basically you know a computer sees the world differently than your eye your eye look out a window we build a 3d model of what we're looking at how does computer do it so there's a variety of ways you can do it one is using lidar sensors which spin around biggest company in this space is called Bella died and been doing it for years for defense and aviation it's been around pointing laser lasers and waiting for the signal to come back so you basically use a reflected signal back and the time difference it takes to be billows back it builds a 3d model of the objects around that particular sensor that is the gold standard for precision the problem is it's also bloody expensive so the karmak is said that's really nice but I can't put for $8,000 sensors on each corner of a car and get it to market at some price that a consumers willing to pay so until every car has one and then you get the mobile phone aside yeah but economies of scale at eight thousand dollars we're looking at going that's a little stuff so there's a lot of startups now saying this we've got a new version of lighter that's solid-state it's not a spinning thing point it's actually a silicon chip with our MEMS and stuff on it they're doing this without the moving parts and we can drop the price down to two hundred dollars maybe a hundred dollars in the future and scale that starts being interesting that's four hundred dollars if you put it off all four corners of the car but there's also also other people saying listen cameras are cheap and readily available so you look at a company like Nvidia that has very fast GPUs saying listen our GPUs are able to suck in data from up to 12 cameras at a time and with those different stereoscopic views with different angle views we can build a 3d model from cheap cameras so there's competing ideas on how you build a model of the world and then those come to like Bosh saying well we're strong in car and written radar and we can actually refine our radar more and more and get 3d models from radar it's not the good resolution that lidar has which is a laser sense right so there's all these different sensors and I think there the answer is not all of them because cost comes into play below so a car maker has to choose well we're going to use cameras and radar we're gonna use lidar and high heaven so they're going to pick from all these different things that are used to build a high-definition 3d model of the world around the car cost effective and successful and robust can handle a few of the sensors being covered by snow hopefully and still provide a good idea of the world around them and safety and so they're going to fuse these together and then let their their autonomous driving intelligence right on top of that 3d model and drive the car right so it's interesting you brought Nvidia in what's really fun I think about the autonomous vehicle until driving cars and the advances is it really plays off the kind of Moore's laws impact on the three tillers of its compute right massive compute power to take the data from these sensors massive amounts of data whether it's in the pre-programmed map whether you're pulling it off the sensors you're pulling off a GPS lord knows where by for Wi-Fi waypoints I'm sure they're pulling all kinds of stuff and then of course you know storage you got to put that stuff the networking you gotta worry about latency is it on the edge is it not on the edge so this is really an interesting combination of technologies all bring to bear on how successful your car navigates that exit ramp you're spot-on and that's you're absolutely right and that's one of the reasons I'm really bullish on self-driving cars a lot more than in the general industry analyst is and you mentioned Moore's law and in videos taking advantage of that with a GPUs so let's wrap other than you should be into kind of big answer Big Data and more and more data yes that's a huge factor in cars not only are cars going to take advantage of more and more data high definition maps are way more data than the MapQuest Maps we printed out so that's a massive amount of data the car needs to use but then in the flipside the cars producing massive amounts of data I just talked about a whole range of sensors I talked lidar radar cameras etc that's producing data and then there's all the telemetric data how's the car running how's the engine performing all those things car makers want that data so there's massive amounts of data needing to flow both ways now you can do that at night over Wi-Fi cheaply you can do it over an LTE and we're looking at 5g regular standards being able to enable more transfer of data between the cars and the cloud so that's pretty important cloud data and then cloud analytics on top of that ok now that we've got all this data from the car what do we do with it we know for example that Tesla uses that data sucked out of cars to do their fleet driving their fleet learning so instead of teaching the cars how to drive I'm a programmer saying if you see this that they're they're taking the information out of the cars and saying what are the situation these cars are seen how did our autonomous circuitry suggest the car responds and how did the user override or control the car in that point and then they can compare human driving with their algorithms and tweak their algorithms based on all that fleet to driving so it's a master advantage in sucking data out of cars massive advantage of pushing data to cars and you know we're here at Kingston SanDisk right now today so storage is interesting as well storage in the car increasingly important through these big amount of data right and fast storage as well High Definition maps are beefy beefy maps so what do you do do you have that in the cloud and constantly stream it down to the car what if you drive through a tunnel or you go out of cellular signal so it makes sense to have that map data at least for the region you're in stored locally on the car in easily retrievable flash memory that's dropping in price as well alright so loop in the last thing about that was a loaded question by the way and I love it and this is the thing I love this is why I'm bullish and more crazier than anybody else about the self-driving car space you mentioned Moore's law I find Moore's law exciting used to not be relevant to the automotive industry they used to build except we talked about I talked briefly about brake pad technology material science like what kind of asbestos do we use and how do we I would dissipate the heat more quickly that's science physics important Rd does not take advantage of Moore's law so cars been moving along with laws of thermodynamics getting more miles per gallon great stuff out of Detroit out of Tokyo out of Europe out of Munich but Moore's law not entirely relevant all of a sudden since very recently Moore's law starting to apply to cars so they've always had ECU computers but they're getting more compute put in the car Tesla has the Nvidia processors built into the car many cars having stronger central compute systems put in okay so all of a sudden now Moore's law is making cars more able to do things that they we need them to do we're talking about autonomous vehicles couldn't happen without a huge central processing inside of cars so Moore's law applying now what it did before so cars will move quicker than we thought next important point is that there's other there's other expansion laws in technology if people look up these are the cool things kryder's law so kryder's law is a law about storage in the rapidly expanding performance of storage so for $8.00 and how many megabytes or gigabytes of storage you get well guess what turns out that's also exponential and your question talked about isn't dat important sure it is that's why we could put so much into the cloud and so much locally into the car huge kryder's law next one is Metcalfe's law Metcalfe's law has a lot of networking in it states basically in this roughest form the value of network is valued to the square of the number of nodes in the network so if I connect my car great that's that's awesome but who does it talk to nobody you connect your car now we can have two cars you can talk together and provide some amount of element of car to car communications and some some safety elements tell me the network is now connected I have a smart city all of a sudden the value keeps shooting up and up and up so all of these things are exponential factors and there all of a sudden at play in the automotive industry so anybody who looks back in the past and says well you know the pace of innovation here has been pretty steep it's been like this I expect in the future we'll carry on and in ten years we'll have self-driving cars you can't look back at the slope of the curve right and think that's a slope going forward especially with these exponential laws at play so the slope ahead is distinctly steeper in this deeper and you left out my favorite law which is a Mars law which is you know we underestimate in the short term or overestimate in the short term and underestimate in the long term that's all about it's all about the slope so there we could go on for probably like an hour and I know I could but you got a kill you got to go into your event so thanks for taking min out of your busy day really enjoyed the conversation and look forward to our next one my pleasure thanks all right Jeff Rick here with the Q we're at the Western Digital headquarters in Milpitas at the Auto Tech Council innovation in motion mapping and navigation event thanks for watching