Rethinking Legacy Data Infrastructure with Eon Co-Founders Ofir Ehrlich and Gonen Stein

Non human actors in enterprise security

0:00Up until now, the concerns came from human threats. What we're seeing now on steroids is that the same type of threat is coming from non-human actors, agents that essentially have legitimate access to the environment with legitimate permissions. Fortunately for us, it's a very similar methodology in terms of detecting that and protecting against that, but the velocity of that happening is extreme. Think of the non-technical people. They're not even aware for things like security or compliance or who is going to use this data. Maybe their agent that they are building are using other agents and they're not technical to even understand what it means.

0:35It creates a complete set of actors inside the organization, not bound by the rules of the organization and not necessarily running within the premises of the organization, but handling sensitive data. It's a good thing and bad thing that everyone inside the organization can become builders. We live in very interesting times.

Introduction to Eon and cloud backup

0:59Today I know Pryors were joined by Ophir Ehrlich and Gunnen Stein, the co-founders of Eon. Eon is a cloud backup disaster recovery centric services designed for the AI era. In this discussion, we talk about data, AI, why Google bought out the data of Spirit Airlines out of bankruptcy, and what it means to really manage and use data infrastructure in the AI era. Ophir Gunnen, thank you so much for joining me on the Pryors today. Great to see you. Absolutely. Thanks for having us. Yeah. So one thing that you guys are doing at Eon is, or actually, why don't you give a quick overview of Eon and what it does really quickly?

1:33Because I think that'll set the context for how we think about AI and data and models and fine tuning models. I think there's a whole stack that's built on top of different types of data sets. And so maybe we can start with what you all do. And then I think we'll kind of walk through like how the world is shifting relative to the enterprise data stack. Yeah, sure. So what we do at a high level is we've created a new data foundation that runs in the cloud, and we provide multiple capabilities that allow customers to first map and classify their data across their environment, across multiple hyperscalers and identify what they have, where they have it, what's sensitive, not sensitive, and so on and so forth.

2:12Then we provide an ability to easily ingest that data from all these different sources, structured, unstructured data into this data foundation. And the data foundation then provides a very cost effective way of both maintaining the data for protection and recovery, but also makes sense of the data. So it allows customers to very easily access it, query it, search through it, and apply their AI models and LLMs on top of that data that's ingested from a variety of sources. Yeah. And my sense is, I mean, your starting point was really as sort of backup and data recovery and protection service.

2:46And I think along the way, you kind of realize if you have all this data from a backup perspective and you have all their customer history over all time, you can start using that for interesting application areas. What are, what are, what are some of those directions where you're seeing customers take this, the, the, the sort of full history of data that you all have or represent?

Enterprise data as the new moat

3:02So as you mentioned, uh, when we started, I said that this crazy person starting a non AI company in a world and the AI tailwind became absolutely insane and made sure that, uh, data becomes the most important thing that an organization organization have. When you can think about it, um, models, uh, uh, compute, everything is relatively ephemeral, uh, almost zero switching costs. And those are infrastructure, important parts of the infrastructure for, uh, the industry.

3:34But if you are a company, whether you are a hotel chain or you are a food chain, a technology company, it doesn't matter. The most valuable thing that you have is actually your data. And you see more and more companies finding these out, you know, just two days ago, you saw Google, uh, uh, by, uh, uh, uh, uh, something from the, uh, bankrupt, uh, uh, spirit airlines. They didn't buy airplanes. They bought the data. They bought the data for $10 million because they think it's very important in that, in that perspective, they're using it to train models.

4:06I think the rumor too, is that the other bidder on the data set was Mercor. Right. In terms of the bankruptcy bid process. And so it's interesting. You had multiple different companies in the AI world bidding on a bankrupt airline. Bankrupt airlines, enterprise data set, which is fascinating. Yes. Do you think we'll be seeing a lot more of that in the future? Like, do you think we're going to basically be seeing these like out of bankruptcy data buys? So for, we've seen it for multiple use cases. That's what's really cool about it. And you see Mercor, you see other companies are continuously trying to already trying to buy data.

4:39If you're a tech data CEO today, I can tell you that you constantly get a, a, a questions. Are you willing to sell your data? I knew it all over. And it seems that's going to be a significant, a significant trend as you go. I'm hearing about, you know, a labs going to Wall Street and trying to buy data from a hedge funds and try to understand how to map and analyze companies. So you, you see a data that was accrued throughout the years by companies, which was usually like tapes.

5:16It was usually, you know, sitting on a shelf collecting dust. And all of a sudden this becomes very important. And you see companies now realize that first, what I have today that differentiates me than anyone else is my data. And this data is gold and actually, I can actually leverage that to get more value for my company and to continue building my business. When AI is actually coming and, and, and, and, and, and, and, and in the playing grounds.

5:49It seems that everyone, even large and small companies basically have the same, the same, the same playing field. And the only real advantage that the company have today is of course their people, but also the data that they've approved. Because everyone has access to all of those cool new tools. Yeah, it's become a moat. I guess in terms of that, I mean, people have been saying data is a new oil for a long time. And as I was a little bit skeptical of that statement. Um, but I feel like now what's happening is because of post training and reinforcement learning.

6:24And, you know, there's companies like apply compute and others are starting to provide these sorts of services where you can fine tune models or open source models against specific data sets. Like, it seems like people are trying to optimize these things for their own use cases. I guess in the case of something like spirit airline is a customer support for building like a airline app. Like what, what do you think they're actually going to do with this information? Is it something else? It's the internal documents. Like, I'm just really curious, like what is, what is the, the reinforcement learning or is it like a customer support agent?

6:55Yeah. But I think if, if, if you're a, if you're trying to build agents today and trying to, you can't just build them a lab, you need to train them on, on, on, on new data, on, on some training data. And it's very hard to find very good data sets. You see that hub, you just released a, a, a, a legal data set just a few days ago. And, but you don't find too many good data sets that doesn't look like real synthetic data that can actually be used to really look like the real world.

7:25And I think that spirit airlines can be used both as an airline company, but also as a large enterprise as a place where lots of people work, a, a, a, a lot of, you know, the hierarchy, middle, middle management, top management, and workers working together. And, you know, if you're looking at what other, a, a public data sets that you have out there, there, there are a lot of those, there's the, the, seriously, I'm speaking with companies asking what kind of data do you have?

7:55What do you train on? You'll find stuff. For example, the annual data is out there in public and people are actually using that as real data from a company. It doesn't know how a company works like any reason is it's so very hard to find data that will help you to work like in the real world. Anytime you see someone building an agent or building a new application, you know, most of them don't really work. You have to go to the world. You have to actually interact with real world companies in order to really build something significant.

8:30Now you can do it when you go to customers, they can, you know, buy data and train in-house. So when you first release your products, every new product that you have, it, it, you, you don't have to first interact with customers on your initial interaction. So I think that you're going to see more and more of that, both by creating new synthetic data, new synthetic data in new innovative ways. In addition to getting existing data, whether it's the real data, whether it's somehow massive or think about it, it contains sensitive information like PII, financial information, so on and so forth, and actually be able to build real world stuff on top of that.

9:11Yeah. And Google obviously is, it's not, it's not, they're in this travel space for a while, right? They, they want this type of data. They're already monetizing it. This allows them to understand, train it, understand that, monetize it even further. And it's a unique situation, right? That obviously people want to take advantage of. And I think we're going to see more and more of that in such situations. And regardless of that, customers who have existing data want to be able to unlock that existing data as well.

Building a new data foundation for AI

9:38What, what sort of tooling are you all building at Eon to allow people to make use their data for AI applications? Like, how are you thinking about this problem yourselves or what, what sort of tools are your customers asking for? So let's, let's go back from the, the, the, the problem statement. And why there are so many tools for data and processing. Why, why, why do you need new tools? Isn't it sold already so many great companies throughout the years and everyone understand data is important. So to put it this way, um, back in the days, every data team find their own data, decide what project do they have.

10:13And, you know, get data to do something with that. Very tactical. They were using, I don't know. Some great companies, Fifron, DBT, Monte Carlo, all the data tools that exist, you know, in order to fulfill their tasks. And for some of the data, they didn't even know exist. It was, this was locked. Why was it locked? Because there are multiple business unit owners across the same company. And let's say a, a, a, you're a data team leader in, in some company and you are based in San Francisco, or we are now here in New York.

10:48And both of us are different business unit leaders. And now there's this thing called AI. And even the boss is playing with Chef GPT. So the CEO and the share and the board and the shareholders, they understand that AI is real. So they're coming to you and they tell you a lot. Um, we have a lot of data in the organization. We now realized data is new oil. We can actually activate it with the new tools that we have today.

11:18We couldn't before. Do something with the data, make it useful and use AI for that because it's valuable for us and because it's cool. What can you do? So you say, great, I've done this thing before. I just need to bring to, I know all of those new cool things that coming out every day in Silicon Valley, I can just leverage them. The problem is where's the data. And so you come into us and we have business unit leaders. If you even know us, maybe you don't, but let's say that you find somehow a guy to me.

11:51I'm a leader of a business unit. I have a data probably. And somehow you convinced me to give me access to my data. Now I don't know what data do I have. I have a lot of people working for me. They have data in multiple systems for the last 20 years. Some of them systems that no one really understands where. They contain production data. They contain sensitive information. You know, there's always this server that no one knows what it's doing, but it's connected to the power,

12:22whatever, which is physically that everyone's afraid to turn off because we don't know what's in there. So we have all of that. And let's say that somehow I know what's in there. Now I need to bring engineers and compromise maybe security and compliance and production uptimes. And to extract the data, just to give it to you and storage in a very inefficient manner. It's very hard. We understood that there's a problem with how this works because we have different incentives.

12:57You were tasked with doing that. I'm tasked with making sure my systems work. And I'm tasked with making sure that data is intact. No data is running away. I don't accidentally have the salary of the CEO inside my data. And it's actually going to be used for training or post-training by you. So we at EON solve it in a very different way. We can help you, not me, you, the data team leader. Find all the data that's in organization in a very simple way.

13:29Understand what it is. Classify it. Map it. Understand context layer on top of that. Build a semantic layer. And then be able to continuously bring all the data from me that is relevant without compromising production, without compromise security. Compliance. We're actually keeping audit. And because data is classified, I know that I'm not accidentally going to share with you sensitive information that you shouldn't have eventually in your data.

13:59We can do it in a very cost efficient and performant way. So you can actually do it from all over the place, bring it to you and actually use it. So it sounds like there's three or four things that you're solving for. One is you're aggregating lots of historical and current data for people. Number two is you're able to then mask personally identified information or other fields that they don't want necessarily shared or set permissions on top of that. And then third is it sounds like all this can then be exposed into AI models for sort of their uses or applications.

14:30And the key and the key point is that customers already have this data. That's kind of the ironic thing. Customers today already have this data. It's kept in their environment and different forms, but it's locked. It's not accessible. And usually it's very, very expensive. Right. So we're able to take what customers already have converted into this new data foundation format. That's much more stored, much more efficiently and provide the mapping classification access control and connected into the workflows.

15:00How do you think about security? So there's been a lot of news recently about the labs where they'll have agents like it's that escape sandboxes and do all sorts of things. And, you know, there may be broader things afoot in terms of why that's happening beyond just the agent of capabilities. Like who knows how these things are set up or configured or, you know, sometimes a little bit uncertain whether, you know, there's that much how people are pushing these things. But, you know, fundamentally, there's a lot of discussion of like AI security. How do you think about that in the context of the enterprise stack, what people should do or not do, how CISA should be thinking about all this?

Security risks from AI agents

15:34Yeah. So up until now, the concerns came from human threats. Right. So this is not you where customers would come to us and say, hey, we were exposed by this ransomware attack. So during our time at AWS, it was a very large customer that was impacted by ransomware. We thought that they were completely protected using our technology, the disaster recovery service that we managed there. And we learned, unfortunately, that the customer thought that they were protected. They weren't protected because they didn't map and classify and tag their resources properly.

16:06So it wasn't protected. And so 60 percent of the environment was was exposed by ransomware. And that's one of the reasons why we decided to launch Eon and solve that pain point around human threats such as ransomware. So being able to detect when that happens, look for irregular right patterns and entropy changes and things like that, protect against it and then also allow customers to recover in a granular fashion and very quickly. What we're seeing now on steroids is that the same type of threat is coming from non-human actors, from AI agents that essentially have legitimate access to the environment with legitimate permissions into such and such databases.

16:45And all of a sudden, and this now happens very rapidly, a table is all of a sudden dropped. Fortunately for us, it's a very similar methodology in terms of detecting that and protecting against that and allowing to recover. But the velocity of that happening is extreme. Something that I noted is that six months ago, no one would even discuss with me. But a few months ago, pretty much every person I meet, every leader in a company tells me either they are afraid of that happening to them or it personally happened to that person who was speaking with me, which is crazy.

17:28You see it all over the place, you see real fear from a I no longer decide what really running on my data. I don't know no longer understand. I need to be prepared for both external threats because, you know, all the new models make it much easier for attackers to come to me and attack me. But also from the inside with agents, I actually approved running in my environment.

18:00So it's a very, very tricky time. We need to assume breach, whether it's malicious or not, and need to be able to handle it and act accordingly. It's a very weird situation today. Yeah. How do you think about the broader enterprise stack and agents? So, you know, the current stack really evolved around people or humans asking their predefined analytical questions. So we have warehouses, we have dashboards, we have the ETL pipelines, we have BI and agents may behave differently and more dynamically.

18:36They may be able to reason over much larger sets of data. They may have access to SaaS apps and historical data and a variety of other things and then act. And so what do you think changes in terms of how you store access and interact with data in the context of like the agentic world? Or what else do you think needs to change? Do dashboards go away? Like what shifts? I actually think we'll see more dashboards because this will be the only way to kind of let us figure out what they have going on in the world.

19:06Because first coding agents are starting to write most of the code that's running in the world. So that's indirectly. But also agents activating other agents would activate other agents and trying to keep track of the non-human identity or that it becomes almost an impossible task. So many actors inside your organization when it's so very hard for a human to understand the change of responsibility. And this is a part of what you're seeing in the proliferation of cybersecurity companies.

19:39How many cybersecurity companies you see in NHI, in non-human identity right now, an infinite amount. And there's a reason for that. It became a number one, number two problem right now. In addition to that, second thing is endpoint. You see endpoint security, which looked like it sold them so many great companies around it. And they were just, you know, a few years ago when endpoint was a completely different problem with TBRs. And now everything that's happening, you see people are running agents today on the laptops.

20:16And the agents sometimes connected to other networks. And they are connected to, I think, maybe on OpenClaw and connected to your WhatsApp, but also to your internal network and also to other applications. And you see, it's very hard for the VP of ITs, for the CIOs to understand what should they do. On the one hand, they are being pushed, pushed by the board, by the CEO, enable AI in my organization now.

20:49Don't block me. You can't block me. On the other hand, it's so scary. I mean, every person, don't even think of technical people, think of the non-technical people building something with, you know, let's say a lovable or any other software that you have for themselves. themselves putting company data there they're not even aware for things like security or compliance or who is going to use this data and they're all using all of those new cool things so maybe their

21:22agent that they are building are using other agents and they're not technical to even understand what it means so it creates a complete set of actors inside an organization not bound by the rules of the organization and not necessarily running within the premises of the organization but handing sensitive data which is the property of the organization could be exposed to the world it could be incorrect could be incorrectly used and becomes a big problem it's a good thing and

21:53bad thing that everyone inside organization can become builders whether you're a social media manager whether you're a phoenix person whether you're in legal or finance so it's amazing but it's also we live in very interesting times in that perspective how much of the existing data

The evolution of enterprise data plumbing

22:13infrastructure do you think survives all this so you know there's all the etl data engineering infrastructure uh that you know people have been building and deploying over the last you know decade did that stick around does that shift does that change like how quickly does all this up end so you see there's a strong campaigning event to pretty much change everything because that's called the plumbing today is very limited and everyone built a solution to their set of problems so think about what happens now gonen goes downstairs after uh recording this podcast and he really wants

22:50coffee so go to the store and buy and buy his coffee and he puts on his credit card now there's a transaction and this is written in some database somewhere okay right so someone needs to today what they're doing they extracting the data putting somewhere and that's it someone else at some point takes this data and processing some other way and that's it so there's no connection with all of those stuff and every person is very different they don't have the context of what happened before and the reason it wasn't and reason is very simple it wasn't so important before to have all the

23:24context all the data for an organization because you could only do with the data things you really intended to do to begin with so you had a single purpose in your mind when acting on the data today it's very different today you understand that you can collect if you are able to smartly collect and clean all your data and make sure you store it in an efficient manner and if you can activate that efficiently you can let a team go wild with all the data that they have and the more

23:55data that they have and more high quality data that they have and the more context all that data that they have the team hunting that can create wonders and think of things which were unimaginable let's say that there's one person in organization who have all the list of all the people in new york who love burgers and another person organization love who has a database of all the people in new york who love pizza they don't know they can find a a list of all the people in new york who love burgers and pizza

24:25because not they didn't work together now if you use it for poster and use it for a and for the new capabilities you can actually do wonders with that you can actually start asking intelligent questions you data intelligent questions you can start using that for your own purposes and just something that you couldn't do before so you're seeing companies first they're collecting tons more data than before the amount of data being ingested is absolutely insane especially comparing to earlier we see trends

24:58continuously both us and other companies that we're seeing in data you see data is growing out of proportions so much of it and so much of it being generated by those new agents so there's a lot of a lot of noise in the data there's a lot of value and noise as well so you need tools that are able to both understand data from multiple locations clean the noise and make sure all of these data that's been created is actually usable it doesn't apply with the old tools that were very some of them were

25:31incredible fiftron was an incredible company dbt and so on and so forth but very niche very specific tools for that purpose uh so so this creates a uh a very interesting brave new world you've seen companies like databricks you know one of the most incredible companies on the planet in my opinion uh and look at that i have more and more data coming in this is it i don't necessarily know where it is i'll help you catalog the data and make use of that but it's an after effect okay you already

26:05have the data now you need to process that uh but they are reinventing themselves all the time because they understand that more and more data has been generated by agents and they thought the way i see it is if you can't beat them join them we'll build our own agents we'll build our own databases we'll got what we and they want to take charge of how data is being used and data is being created and it's completely different than how any uh other people use that you know just three or five years ago

26:39yeah so the goal is really to enable right enable this culture of of builders and the culture of uh of agents with the ability to automatically help them understand what's there automatically help them ingest the data without having to build manual pipelines for each and every application that is being built and then also help them maintain control on top of the the data that's created makes sense and you see with every data that you have there's another problem right now that lots of data is amazing but it's scattered which is a sound of problem but then you need to

27:12access that you need to pay for that for storage and of course tokens and we're not in the time of a token maxing anymore you know trying to go to actually getting value for every token that we have because it becomes more and more and more and more and more and more more expensive so you want to be very wise in you don't want i don't want to say not paying millions pay millions and even more than that if you need to but get the value that you can from actually doing so so it's very expensive very

27:46lucrative let's make it relatively as least expensive as you can have it so i guess um you know the other

Comparing the cloud and AI transitions

27:54thing that you guys have really lived through is the cloud transition so prior to eon you started a company called cloud endure that was acquired by aws and aws you really saw that migration uh from on-prem to the cloud at like a huge scale in terms of that that big sort of generational shift that had happened before this how would you compare this infrastructure change to what's happening with ai right now like what do you view as sort of the cloud era versus ai era and what are takeaways or lessons that you can

28:25apply across them yeah i think uh again it's uh it's like that but on uh but on steroids uh and even before we we sold our last company cloud into AWS we uh supported similar uh large-scale enterprise migrations with the other hyperscalers with uh with azure and with gcp where our product was uh was integrated oem into the console uh so very large enterprises that were moving uh thousands tens of thousands or hundreds of thousands of servers and then we saw those modernized further in uh in the cloud uh and after we sold to AWS we did that as part of the application migration service uh but that's

29:00kind of where it ended and it required a lot of work a lot of effort both from a technology side as well as from uh from the human side what we're seeing now in the this crazy world of uh of ai and agents is that those transformations are happening way faster and uh and customers are losing control to a point where that's becoming an inhibitor right not an enabler they're stopping they're pausing because they're afraid that things might break that data might leak that uh ip might uh break out and uh and so they're looking desperately for this level of uh of understanding of what's happening and control

29:33so it became so insane that and and so fast and one of the reasons is that cloud in my opinion cloud is somewhat somewhat abstract because it's very hard to explain what it means cloud basically just someone else's computer but who knows what it is it's hard to explain to a my grandmother about a cloud AI everyone understands AI everyone under everyone lived from the chat GPT moment when we all asked what they could do oh my god this is incredible so they're getting pushed by by sea levels by CEO by the board

30:10by the shareholders use AI for the business otherwise otherwise we're relevant so you see people doing it both for the value that you get from AI also for the from the fear that you get from my eye and you see new trends of for a first time in in a lot of in in in many years you see how companies consume software in a brand new way and one example is uh what's happening with forward deployed engineers used to be something look like

30:45services penalty we're doing that no one really didn't understand what it means and now everyone's doing that now it seems that you're coming to a large legacy enterprise they really want to adopt their because they have to the problem is they don't know how to do it they understand that their processes are very long there's some that takes a year or or two or more but they need to have it now and the only way they can actually getting deployed and and and become AI much faster is by letting

31:21strong engineers who understand what they're doing and coming with the toolbox that they've created in top silicon value companies startups and sometimes larger companies to come and transform those organizations and you see them shrinking sales cycles and you see companies going really fast because of that you also see companies buying really fast especially the new companies

31:51buying using product-led growth by in really really fast a a a a a infrastructure with which actually helped to build agents because everyone now wants to be the agent now in the past i was arguing that for the majority of things plg doesn't work especially for dev tools and because what's very fragmented people don't want to move so fast so forth so forth now it became super hot looking companies like cognition for example which is you know incredible company that were able to first go through a a a a a plg we use that

32:27that way in eon and and then through the fd motion turning going to banks and then will a replace engineering that you don't want to do with our engineers making you focus with the things that you do want to do so leveraging on all fronts and so it became super super super interesting the world is changing so much and one other really interesting way that companies are leveraging ai is it's they are very slow to adopt ai but there are really great companies for example long lake and that to say instead of

33:04you adopting ai i know how to do it more efficiently if i can buy the company and transport it into an ai company we can all win we can create an avatar make a higher margin more efficiently and this is a really radical new way for those companies to actually start using ai become more efficient and we speak about this as a revolution but i think we just started most companies still don't use ai most companies still at the beginning of this journey they all understand that something is happening they understand the data is important they all understand that their existing processes are somewhat

33:39a a mundane and they need to do something about it but it's scary but you have to do it so it's a it's a fascinating thing to see it's a fascinating and fascinating evolution on what's going on right now in how companies consume ai software how console companies transform into being more modern how much actually being pushed to do that and i think that eventually i know it's a very wild ride but i think

34:12everyone is going to go the world in my opinion is going to be better and because of that amazing well thank you so much for joining me today a very interesting wide-ranging conversation on data and ai really appreciate it thank you our pleasure i thought it was a pleasure and all right thank you thank you find us on twitter at no priors pod subscribe to our youtube channel if you want to see our faces follow the show on apple podcasts spotify or wherever you listen that way you get a new episode

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