 Okay, welcome to theCUBE, everyone. This is theCUBE conversation here in Palo Alto, California, the CUBE studios. I'm John Furrier, the co-founder of SiliconANGLE Media, co-host theCUBE, I'm here with England, she's the co-founder and CEO of DataVisor, entrepreneur, former Microsoft researcher. Thanks for joining me in CUBE conversation. It's my great pleasure to be here. So I'm excited to chat with you because you've got a really hot company in a very hot space, but also as an entrepreneur, you're out competing against a huge wave of transformation. You've got the big clouds out there. You've got IT enterprises moving to some sort of cloud operating model. You have global IoT market, huge security problem. You guys are trying to solve that with DataVisor, your company. So take me through the journey. For first, take a minute to explain what DataVisor is, and I want to ask you about how you got into this business, how it started. So tell us, what does DataVisor do? First, give a one minute overview of the company. Sure, so DataVisor is a company that uses AI, machine learning, and big data, trying to detect and prevent a variety of fraud and abuse problems for all these consumer-facing enterprises. So our mission is to really leverage these advanced technologies that you talk about in many of these, and to help these consumer-facing enterprises to establish and restore trust to the end users like you and me, like every one of us. You know, cybersecurity and security in general is a global issue, and I mean, spear phishing is just so effective. You can just come in and just send someone a LinkedIn message or an email. They click on a link and you're done. It's not much technology. These people are struggling with this, but you guys have a unique approach that you're taking with DataVisor. So I want to dig into it, but first, how did it all start? Was when you started this company with your co-founder, did you just wake up one day and say, you know what, we're going to go solve the security problems for the world? Did it come, where did it, where did the idea come from and how did it all start? So I would say it's probably like sort of, if you look at the background of me and my co-founder, it's probably the natural journey to it because we actually came from like a research and academia background. Being spending seven years in Microsoft Research, Silicon Valley before starting DataVisor, and there when we joined like 2006, actually it was where we kind of just see this parallel computing paradigm like MapReduce Paper just got published and all the data is available. We have all these security problems and that time we're partnering with a number of large consumer facing groups in Microsoft and to see how we can use this big data to solve some of the challenges that they face in terms of for example the online fraud and abuse. And also we see the industry and was rapidly getting into the digital era where we have like billions of users online. So everybody see this unique challenge if they have a variety of vulnerabilities they face, they're trying to bring more rich features to users but at the same time they see new fraud coming up also like sort of very rapidly. So everybody when they see new fraud they're trying to have a point solutions where they say let's just tackle this. But then afterwards there's another fraud or another abuse coming up. And so with those problems. Throw another tool out. It builds another tool. Find another tool. Exactly, kind of arms race. Always being kind of a little bit reactive in catching it in a cat and mouse game. So we decided that well let's just come to see whether we can build something different and leverage like AI machine learning. And then we see like what this new kind of cloud computing big data infrastructure can do. So let's build something a little bit more proactive so that we've been in the security area for so long that we feel something fundamental that can be game changer is only when we don't make assumptions to see what kind of attack we want to detect. But be a little bit more open to say let's try to build something more robust and now can have the ability to automatically discover and detect these like new type of unknown attacks in a more proactive way. You know, England I want to talk about that point about your time at Microsoft. At that time around 2006 I think it's notable because the environment of Microsoft scale was massive. They were powering, the browsers were everywhere, MSN, the online services that Microsoft had were certainly large scale but they were built on what I would call gen one internet technology databases you know, big large scale. At that time they're the new entrance Facebook of the world. They were building all their own tech. So you had kind of the new entrant who had a clean sheet of paper and they built their own large scale. We know what the history of that those kinds of companies that were natively at that time. That's the environment that Microsoft had that a lot of customers today have. Technologies that have been around they have to transform very quickly. So when you learned about some of those data collection capabilities at scale of older technologies and rushing to a newer solution this is a problem that a lot of end user enterprises have. CIOs, cloud architects, data architects and they've been operating data warehouses for generations. Big fenced off databases, slow, big data lakes turning into swamps. So that's the current situation. How do you guys speak to that? Because this is the number one challenge we see is I have all this data. I got a data problem. I'm now full of data. I'm being taken advantage of with the fraud whether it's spearfishing or some other scams that are going on without email and all this stuff. How do you guys talk to that customer that environment? You definitely very spot on the challenges and problems everyone face. So while we get into the digital era everybody has this great sense of trying to collect data and store those data. So that has been the amount of data we collect is tremendous nowadays. The next step everybody was looking at the big challenge for us is how to make value of these in a more effective way. And we also talk about like lately a lot on this AI and machine learning. Those type of newer technology how they can transform some of the way we do things in the past. Like how the analogy we know is how do we go from the manual driving cars to the self driving like era of having all the automation intelligence making value out of this. So there are still a lot of challenges that you definitely touch upon. First of all when they have the data there does that mean we have the data? We have the data in a consistent consolidated way. Many times still different divisions, department collecting data they're still in silo mode. So how to bring the data together. And second is that we have the data. We have the computing power. How do we bring the algorithm that operate on top of that framework to have a system that would let the algorithm like sort of generating values like us for example in the fraud detection like space be able to automatically process huge amount data and make decisions in real time instantly of detecting these new type of attacks. So we find that's a problem beyond the silo of we say just IT problem and or just a data science problem or just a business problem. So many times these three groups still like sort of work separately but in the end we needed the main knowledge. We need the building a system and we need good data architecture to solve them together. So that's where data visor is building kind of a solution the ecosystem to consider all of this. Okay so let's talk about the ecosystem a little bit later I want to get to the algorithm piece because I think that seems to be your secret sauce right the algorithms. Is that where the action is for you guys the secret algorithms or is it the setup and the environment first. I mean it kind of makes sense you got to set the table first get the data unified or addressable and then apply software algorithms to them. That's what's your secret sauce. Yeah so that's a good question. A lot of our customers ask us the same question is algorithm your secret sauce? And then my answer is kind of partially yes but also at the same time not completely because we are all catching up very rapidly in our algorithm if you look at the new algorithm being published every year there's a lot of great ideas out there great algorithm there and so our unique algorithm in this differentiating technology is called unsupervised machine learning where so unsupervised means we don't need to require customers to have historical loss experience or need to know the training labels of what past attacks to look like so to proactively discover new type of unknown type of attacks in an automated way. So that's what the algorithm part is and it has its merit. And by the way people want to know about the machine supervised and unsupervised machine learning you go Google search some papers out there but I think most people might know this it's really hard to do unsupervised machine learning because supervised you just tell it with a look for it finds it unsupervised as saying be ready for anything. Because it's very simple. So exactly unsupervised means we want to make decisions without assumptions and we want to be able to discover those patterns as the attackers evolve and be very adaptive. So that's definitely a great idea out there I wouldn't say like if you Google like search unsupervised and you would find so I think academia they're all published like algorithm about it, et cetera. And so I wouldn't say it's a completely new concept it's a concept out there and but if you look at it from- It's been around for a while but the compute is the value because now you have the computation to accelerate all those calculations require that we used to be stalling it from 10 years ago. I mean it's been around for a couple of decades AI and machine learning but it's been computation insensitive. Very much so, very much so. So if you look at the sort of the gap where like sort of is from for example the academia side of the word algorithm to where it's working it is something similar to deep learning requires a lot more like a computation complexity compared to the past algorithms I got to ask you because this comes up and let's get back to the reality of the customer because I lack and geek out on this all day long I love the conversation and we should certainly do a follow up on deep dive with our team but the reality is customers have been consolidating and outsourcing IT for generations and just only a few years ago that they wake up and some woke up earlier than others and said wow I have no intellectual property I have no competitive advantage my IT's all outsourced I am getting killed with requests for top-lying revenue growth and I'm getting killed with security breaches and where's my IT staff? So they don't have the luxury of just turning on a machine learning hey give me some machine learning guys and solve the problem that's really hard to set up you got to kind of build a trajectory with economies of scale in IT this is a huge problem how do you work with companies that just say look I got security problems but I don't have time or the capability to hire machine learning people because that's an aspiration that's not viable not attainable what do you say to the customers can you still work with those customers are you a good fit for that kind of environment talk about that dynamic because that seems to happen a lot Yeah so in that area like sort of you really want to bring to the customer a solution that solves their problem not like us today we have a lot of like sort of infrastructure capability like platforms where they can leverage but you definitely talk about the challenge they face they don't have people to leverage those underlying primitives and build something that immediately address their business challenges and that's where DataVisor is to provide the platform and the service to the customers where we take data in and tell them directly on the kind of the type of attacks they face in real time constantly all the time I really want to get your opinion on something I've been talking about publicly lately and I've been interviewing folks in the industry about it because you know if you look at the graphics market with around AI NVIDIA has been doing very very well they broke into gaming obviously it's vertical and then they've been using the graphics cards for blockchain mining and then so NVIDIA kind of walked into these new markets because they had a general not general but a purpose built processor for floating point and graphic stuff that was very specialized but now it becomes very popular we're seeing the need for something around data where you have you want to have agility but you also want high performance so people are making trade-offs between agility and high performance and if you ask anyone they'll tell you that I'd love to have more performance in data and so there's no NVIDIA yet that has come out and become the NVIDIA of data there's no data processing unit out there yet this is something that we see a need for so coming in, what you're talking about here is customers have all these demands it's almost like they need a data processing unit and what they need is a solution like you said when they have a business solution they're not looking at something like a generic framework or a generic paradigm they're looking at something that tackle the specific need like we're talking about for example when we talk about fraud prevention we're talking about we're building a service ecosystem that combines the data element combines the algorithm that address their problem right away so that's what we're talking about with your analogy of NVIDIA they want something almost like that chip directly solve their pain point and that's what you guys are kind of doing because if I get this right you guys have the kind of horizontal view of data but you're going very vertically and specializing on the vertical markets because that's where the need for the acute nature of the algorithms to be successful like safe financial services am I getting that right so it's like horizontally scalable data but very specialized purpose exactly so horizontally scalable data but then really mine the data and build the algorithms that optimize for the detection of these unknown type of fraud in this area because they're customized I mean they have this certain techniques that the financial guys will use to attack the bank so that's kind of like that you have to be really nimble and agile at the application level right so when we build the algorithm we have in the mind the specific application we need to target so you don't want to be over general in the sense that it can do anything but in the end it does nothing super super well so if we are solving that particular like sort of fraud detection problem in the end it needs to be everything needs to be optimized integration with data the algorithms, the output the integration with the customer needs to optimize for that scenario in the long run can it be even generalized you can talk about the agility and the nimbleness to broaden out to other areas then I would say like we are taking approach I would love to see for example Nvidia's approach gradually then expanding to other verticals that is something we're looking from the long term perspective but any time our view is that we are a layer above all the cloud computing the data layer we are the layer that is verticalized position and targeted to solve this specific business issues and we want to do that really well solve that problem once at a time and then leverage an algorithm the underlying infrastructure we build to see whether we can expand that to other verticals other scenarios So you don't get dependent upon the cloud players you actually will draft off their success So we leverage cloud computing aggressively who doesn't like in this scenario they definitely bring the scale the agility and the flexibility to expand and there's a lot of great technology What do you think about the cloud players when I see your customers that you have I want to get into that thing but when you look at the multiple clouds multiple clouds and hybrid cloud there's a trend happening right now What's your opinion of how that's going that comes up a lot CIO's number one challenge is cloud architects and then data architects are all kind of like working as kind of the new personas we're seeing How is the cloud and multi cloud or single cloud approach for your customers how do you see that evolving because we see trends where for instance the Department of Defense is probably going to go all in on Amazon that's a single cloud solution but it wasn't sourced as a single cloud so it turns out that Amazon was better for that but they'll probably win everyone's kind of talking about that but versus spreading things around the multiple clouds So there's a trade off What's your thoughts on that as a technologist Well you touched upon an interesting point because we actually our position is multi cloud multi cloud as well as we support even like on-premise deployment and I'll talk about the reason why the cloud is such big space and we see different players there we definitely see like different players because of for example their historical kind of for example working with different kind of vendors as well as their development that you definitely see and so from actually our position in this space was literally driven by the customer need from that what we saw is customers have these requirements for example their favorite cloud environment and then there's this public cloud versus private cloud I will not complete there to say one cloud that rules all and then you also see some very conservative areas particularly financial services where data security is really somebody like sort of still like sort of really the top priority the conservative and from that perspective they still are having on-premise solutions and we have to be considerate of all of these different kind of requirements and also when we look at the environment we also see different geographic landscapes have different kind of sort of the cloud deployment landscape as well and it's a dynamic environment and... It's a new dynamic it's a new dynamic it's a new dynamic especially the global component, the regions Exactly the regions and the different regions now we also have a GDPR where there's a data residence problem so that also makes it also challenging to say just deploy your solution on one type of cloud and that's very rigid model and so definitely from very early days we basically decide our data decision will be we are going to support multi-cloud very early on It makes sense because people don't want to move a lot of data around they're going to want to have data in multiple clouds if that's where the app is the latency and the threats around moving packets from point A to point B are a risk too not just the latency but acts All right so great I'm very impressed with your vision very impressed with what you guys are doing I think it's very relevant talk about the business what are you guys at in terms of customers what kind of customers do you have how many customers can you talk about some of the metrics how many customers you have what kind of customers what are they doing with you what are some of the successes can you lay out some of the use cases? Yeah so we work with many of the largest enterprises in the world and so they probably also like sort of the ones that face a lot of challenge of these large scale fraud at the same time and they are the ones also aggressively moving forward in adopting new technology solution they're a little bit more kind of the earlier kind of pioneering kind of adopters so our customer can be in three verticals today like so we take a vertical approach the first is those large social commerce like sector and some of our customers for example in crude for example yeah I'll pinch this kind of customers and there is also the second vertical is those mobile app and where a lot of there's a lot of fraudulent in stores where these mobile apps and we're trying to acquire users aggressively everywhere but among the users acquired those in stores there can be substantial amount that is fraudulent so those are the separate like sort of a segment we target and the third segment we talk about you mentioned is the financial area where traditionally people focus a lot on kind of the risk of control the fraud detection definitely constantly being problem their challenge is when they move from the past like existing like sort of area now to the digital area going online and a lot of new attacks that are coming up and definitely huge challenge problem for them as well so you guys have some great funding some great investors NEA and United Parties Associates the Sequoia Capital what's the growth plan for you what's the goal for the company what's your growth strategy what's on your mind now hiring obviously customer what's the focus what's the growth plan so our focus is we've been working with many of these large service providers as we mentioned our large like enterprise customers so globally today we've already been protecting over kind of a full billing kind of an user like accounts like in total so it's a lot of users this moment for our next step of growth and so we have like sort of two kind of thoughts A is we want to basically make it the the service even more scalable and even more kind of standardized in the sense that we can work more than just the largest ones and be able to make it convenient to be integrated with as many for example consumer facing kind of these providers to expand the breath the breath basic of the customers that we work with the second aspect is we're looking at the fraud detection we feel in traditionally when the fraud market is segmented we talk about when it in offline world and you would see financial sector fraud very different from for example somebody working on content nowadays become consolidated so in that area we're trying to build a more kind of holistic kind of ecosystem where for example the device side of solutions and the analytical solutions can be consolidated together to make it an ecosystem where we can basically have both side of views and being able to provide to our customers different kind of needs and in the past it was very point solutions you would see kind of a data signal like sort of providers then you would see some algorithm providers and focusing on specific type of fraud and we wanted to make it ecosystem so that to your point in the past on the data we will be able to connect the data look at the user account level and be able to detect a variety type of fraud as for example the enterprises are pushing on new features and the new flavors of these type of fraud and the ecosystem participants will look like what ad networks, data sources who's in the ecosystem that you want to build yeah so that's a great question in the ecosystem we talk about for example cloud providers can be ecosystem basically they actually power the computation kind of the layer of all the the resource there and we can also partner with data partners that's another important element so you're looking at the technology data system or integrate together at the same time we can also look at for example the consulting firms that bring a bigger solution to the customers with the fraud being important component that they want to address system integrators and so all these can fit together and even for example some of the underlying for example the algorithm solutions in the end can be plugged into the ecosystem to provide different aspects of views and make make value out of data and so that different algorithm work together and become a better kind of defense area it's like a security first strategy we heard first we had cloud first data first now security first I mean got to have the security well I really appreciate we need more algorithms to police the algorithms the algorithms for algorithms so maybe that's next for you guys well with the business go in mine right we always take a kind of open holistic view like I like you who are talking about security first when we look at this how to solve that problem more effectively then we are very open minded to say what is best combinations we want to be there ultimately and that's a single bit of real-time instant decision that is important at that time because that matters with good users friction they face whether we can be able to accurately detect attackers so we're all optimizing for that and then all the underlying data consolidation piece the algorithm like sort of combination working with each other is just to make the barrier high make it difficult for the attackers and make all of us good users easier well you're doing amazing things and I think you're right there's value in that data new ways to use that data for better security it's just the beginning that this is just the beginning of this new trend thanks for coming in and sharing your insights and congratulations on a great start-up and good luck to you and your co-founder thanks for sharing thank you great to have this conversation I'm here in the CUBE studios in Palo Alto I'm John Furrier for CUBE Conversations with a hot start-up data visor Inc England Chic CEO and co-founder I'm John Furrier thanks for watching