Video: The next stage in cybersecurity is an AI-powered data-centric model
Watch the video interview above or read the full transcript below.
Jason Hiner: Hello and welcome to RSAC TV. I’m Jason Hiner, the host this morning from ZDNet and TechRepublic. Here, we’re going to talk about this week lots of security experts, lots of great advice, lots of wisdom, and talk to some really smart people to help us learn a lot more about how to make cybersecurity better. Our guest this morning is Yaniv Avidan, the CEO of MinerEye. Yaniv, welcome.
Yaniv Avidan:How you doing?
Jason Hiner: Very good. Thanks for being here. You are the CEO of a company that’s doing some really interesting things to make security better, make cybersecurity better and to solve one of the most persistent problems. Let’s talk a little bit though about the state of security and how that got to what you guys do. We’ve been working for the past decade and more in security industry on moving from this model of network security where you just secure a perimeter and then once you get inside you let the people inside then you have access. That hasn’t worked real well as we’ve moved to mobile devices and IOT and lots of other cloud and more a newer architecture. It’s been more of this data centric security where you identify your most important assets, but now it’s moving to the next stage, which is more like AI powered security or machine learning.
Talk to us a little about it. You’ve worked in the security industry for a long time. Tell us a little bit about that evolution.
Yaniv Avidan: Yeah. First, thank you for having me, Jason, and I’m very thrilled to be here. I think we’ve seen that gradually evolving very parallel to how data evolves within companies or within the enterprise. We see, as you said, more and more platforms entering into the enterprise scene. We see also data evolving and many data formats evolving and new data coming in. We saw data piling up in an exponential phase. I just read some stats about it that the past two years has been almost 90% of the data that have been created ever. Those three aspects of how the compute world and how people use data and how data becomes centric within our life in making decisions, extracting value, moving fasting with business actually shaping the security arena and shaping the way we consume security.
One of the things that now really driving back from old discussions is GDPR, actually, or all the privacy stuff that we see in TV with all the Facebook stuff that we see. This keeps bouncing back, all the privacy. On one hand, we share data very quickly. We want to move quickly. We want to use as many channels to exchange data and exchange information. On the other hand, we’re getting more and more sensitive about our own private information. Those are not necessarily contradiction vectors. Once you use some sophistication around artificial intelligence and identification of sensitive data discovery, you can actually work those two together.
Jason Hiner: You noticed … You’ve worked in the security industry, worked on security in the tech industry for a long time and one of the things that you noticed in working there was that there was a problem in the way that we approach it and that it was important to be able to change the paradigm in order to better protect the company’s most important assets, to take more of a risk management approach to security than just constantly reacting and chasing bad buys. Tell us about that.
Yaniv Avidan: Yeah. It was back then in my Intel days. I was hired as a guy that had experience around data mining and machine learning to actually see if there’s a way to harness data to identify those actors, those attackers that either are already in or trying to get in and so on and so forth. Back then, most of the security was network oriented and less in data. I was lucky enough to hook up with the best minds, not just within Intel but also with [inaudible 00:04:59] and stuff like and learn this very thoroughly. We formed a team of data scientists back then and subject matter experts to actually crunch a lot of information and find those bad guys. Now, we were very successful compared to other solutions back then.
Jason Hiner: Sure.
Yaniv Avidan: It was a new high, but then it sparked my mind that the tactics should change. I just talked to my manager Malcolm and I ask him why are we keep on chasing those bad buy when we know they’re always two to three steps ahead us or even more than a few months already in doing what they want? Instead of putting all our effort in identifying those crown jewels isolating them and then focusing the security controls to secure them. I think we’ll be much efficient in doing that. We’ll be much more forward looking as data evolves and networks evolve and create some better solutions going forward, so that’s how it started.
Jason Hiner: Very good. Let’s talk more about that in a minute, how that led to you starting MinerEye. Let’s talk for a minute about you mentioned GDPR.
Yaniv Avidan: Yeah.
Jason Hiner: GDPR is eminent. Next month is when it arrives. There’s still some confusion around it, but you have a lot of people that come to you wanting help with this and they see what you guys do as a way to help them. When you look at GDPR and you think about it compared to other governance systems, what do you think the impact is going to be and what’s the impact you see on customers that are coming to you wanting help with it?
Yaniv Avidan: Yeah. As you said, there was more confusion than certainty on what’s going to be. I don’t know what the deadline will be. My bet is that at least the European will find some test cases just to show that there’s some teeth behind this. But, I think everybody and for and foremost our customers should look at this as an opportunity rather than as a deadline and focus less on the legal definitions of GDPR. I know it’s not well defined sometimes. There’s a lot of holes or confusion or some vagueness around this, but take that as an opportunity to improve information governance as a whole. Because this is a building block, as I said before, in doing almost everything not just about reducing risk around data, improving our security, information security, or privacy, it’s also about extracting value for our businesses.
Jason Hiner: Sure.
Yaniv Avidan: That’s enabling those business to run faster, make more money, and that’s what I’m talking about. There’s no contradiction between data privacy and protection and moving faster with a business making more money. That’s how I think businesses should look on that and the most important thing is not doing the same mistakes again that was done 20 years ago. Just look forward.
Jason Hiner: Yeah. When you think of … Do you see companies coming to you? Is it mostly companies focused on their European operations of European companies or do you see multinational companies looking at GDPR and saying we’re going to take this as the opportunity to just improve governance across the board, not just in our European operations?
Yaniv Avidan: What we see here is multinational companies, especially big companies that have the resources and the teams to check on new technologies, that they have the breadth to do that. Yeah. GDPR is a driver, but we see effect in the US privacy. We see even state level [inaudible 00:08:57] coming in, also in California and New York. We’ve seen this happening, but most of those customers are multinational but are again taking the opportunity and the budget provided them by the board thanks to GDPR to improve their privacy posture but also do some stuff around new technologies and solve big problems around this.
Jason Hiner: Very good. All right. Let’s talk a little bit about MinerEye and the solution that you created to deal with this more proactive approach to security and to what you call the crown jewels, your most valuable data, most valuable assets, digital assets in the company. What did you do? Talk to us why you created the company and then what solution that you guys offer.
Yaniv Avidan: Yeah. As I told you, it started from the point where I wanted to replace the chasing the bad guys and identify the crown jewels, but the main thing I asked myself is how can we make things much easier to our partners to consume this technology rather than doing the same old stuff like identifying or defining rules, keywords, dictionaries, maintaining that, do a lot of manual work here. That’s where machine learning comes into place, but we added another thing that actually acts as a very unique approach and this is how the machine identifies the data, which is the very basics behind our technology. That’s where my partner [inaudible 00:10:35] worked for me in one of the former work I’ve done Israel Minister of Defense.
He was actually … He developed algorithms around tracking targets on a video stream.
Jason Hiner: Okay.
Yaniv Avidan: I asked him if you can track targets, why can’t you track sensitive data on a network? That’s how the idea … It was completely off right then. After 18 months, we actually had been able to convert technology from totally different use case, military use case into that specific domain of identifying the data automatically and actually tracking the data wherever it resides in whatever form it actually has. That’s the beautiful thing about it. Today, this platform actually can be trained by a normal guy that has no data science background by just providing some example of what he considers sensitive data and some state’s definition of that data and that actually acts as a training set for the system.
From that point, the system is totally autonomous in identifying the data and importing or even triggering security controls to act upon those identification. The best technology I can provide for this is think about your own kid, the first time you trained him how to cross the street for instance. You identify the cross road and you identify some states in which the road needs to be in order to cross. Your kid is smart enough to know that after one example, two examples, and that’s what actually we created. This system needs very few examples of specific data domains to actually create its own taxonomy and classification and tracking of this data.
Jason Hiner: Your solution is focused on identifying, tracking that data and then interfacing with other systems that are triggering the protection controls, encryption and those kinds of things, so it’s a solution for both data in rest, data in motion, and even during migrations and those kinds of things.
Yaniv Avidan: Right. We see multiple use cases that follow the common sense rule. First, they use the solution to minimize the data and that’s, by the way, explicitly [inaudible 00:13:05] by GDPR. You need to get rid of the noise. You need to identify those redundant pieces of information that have been accessed, have been touched for a long time and are duplicates across your environment. That’s very easy for the system to identify and track.
Jason Hiner: MinerEye will help you identify this?
Yaniv Avidan: Exactly. Just start off with this. By the way they gain immense value right after a few hours by clearing huge amount of space in their desk. Think about it. Millions of dollars of saving, so it pays off really after a few hours. The next stage would be to actually identify the data and classify the data either using our own classification capability internally or using external labeling mechanisms such as Azure or any other capability that we interface. Having said that, the system is connected also to other security controls who act upon those lists of data that it identifies. The third use case is to be able to segregate the data. I want to make sure the data doesn’t leave a specific geography, hence GDPR or any other privacy solution or just one internally, the data will be segregated and not be able to be accessed by people that shouldn’t access the data.
Again, all goes back to the ability to identify data in its dynamic state at all times continuously, classify it, and act upon it.
Jason Hiner: A customer buys your product, how long does it take? How onerous is it to get it up and running? Whether it’s a big company, little company, is there a difference even depending on the size of the company, amount of data, all of that?
Yaniv Avidan: Yeah. [inaudible 00:14:58] installation is about 15 minutes.
Jason Hiner: 15 minutes?
Yaniv Avidan: Yes. It’s about 15 minutes configuring it. It does not require more than [inaudible 00:15:05] permission over your repositories. There’s not local installations of agents. It’s all done from remote. This is key element. The learning phase depends on the number of files that you go through and the resources you allocate. We designed this not just to be very easy to install for the IT guys, but also to configure it and distribute the solution according to the network architecture. That’s very easy approach, plus it’s all contained in a virtual appliance that all technologies contained inside the [inaudible 00:15:44] inside. They don’t need to manage data bases or versions of operating systems and so on.
Plus, this all in your virtualization environment. Again, you don’t need to come up with appliances. All you need is to allocate the compute resources and kick it off. Very easy to install, very easy to maintain, and very easy to extract value immediately right after the first run of the system. We put a lot of effort in designing the solution this way.
Jason Hiner: Very good. Machine powered, data centric security that will help you with GDPR and that will help you be much more proactive in the way that you protect your network.
Yaniv Avidan: Yeah.
Jason Hiner: Very good. Yaniv, thank you for your time.
Yaniv Avidan: Thank you very much.
Jason Hiner: Appreciate it.