Rethinking Legacy Data Infrastructure with Eon Co-Founders Ofir Ehrlich and Gonen Stein Transcript, AI Summary & Key Points

00:00 Up 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.

00:18 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. It creates 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.

00:46 It's a good thing and bad thing that everyone inside organization can become builders. We live in very interesting times. Today on no prior we're joined by Afair Erlick and Gunnen Stein the co-founders of Eon. Eon is a cloud backup disaster recovery ccentric 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.

01:20 A feel Gunnan, thank you so much for joining me under prior today. It's 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 because I think that'll set the context for how we think about AI and data and models and fine-tuning models.

01:38 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 to the enterprise data stack. >> Yeah, sure. So uh what we do at a high level is we've created a new data foundation that runs uh in the cloud and we provide multiple capabilities that allow customers to first map and classify their data across their uh environment across multiple hyperscalers and

02:05 identify uh what they have where they have it what's uh sensitive not sensitive and so on and so forth. Then we provide an ability to easily ingest that data from all these uh 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.

02:27 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 uh 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 and 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.

02:54 Um what are what are some of those directions where you're seeing customers take this these the sort of full history of data that they that you all have or represent? So as you mentioned uh when we started I said I'm this crazy person starting a nonAI company in an AI world and the AI tailwind became absolutely insane and made sure that uh data becomes the most important thing that an organ organization have when you can think about it um models h compute everything is relatively ephemeral almost zero switching cost

03:28 and those are infra important part of the infrastructure for uh the industry. But if you are a company, whether you hotel chain or you are food chain technology company, doesn't matter. The most valuable thing that you have is actually your data. And you see more and more companies finding this out. You know, just two days ago, you saw Google buy a something from the H bankrupt H Spirit Airlines.

03:57 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 perspective. They're using that to train models. >> I think the rumor too is that the other bidder on that data set was Merkor, 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 airlines enterprise data set, which is fascinating.

04:23 Yes. >> Do 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 Mercury see other companies are continually trying to already trying to buy data. If you're a tech data CEO today I can tell you that you constantly get questions.

04:44 Are you willing to sell your data? I hear it all over and it seems that's going to be a signific a significant trend as you go. I'm hearing about, you know, labs going through Wall Street and trying to buy data from hedge funds and try to understand how to map and analyze companies. So you you see a data that was accured throughout the years by companies which was usually like tapes.

05:15 It was usually you know a a 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 it to get more value for my company and to continue building my business when AI is actually coming and and and and and uh flatten the playing grounds.

05:48 It seems that everyone can start even large and small companies basically have the same the same the same playing field and the only real advantage that 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 the I mean people have been saying data is a new oil for a long time and I was always a little bit skeptical of that statement.

06:16 Um but I feel like now what's happening is because of post- training and reinforcement learning and you know there's companies like applied comput 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.

06:34 I guess in the case of something like Spirit Airline, is it 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 sort of curious like what what is the the reinfor reinforcement learning or is it like a customer support agent?

06:54 Yeah. But but 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. 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 Harvard just released 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 and I think that spirit dines can be used both as an

07:27 airline company but also as an large enterprise as a place where lots of people work a lot of you know the hierarchy middle management top management and workers working together and you know if you're looking at what other h public data sets do you have out there there aren't a lot of those there's the the seriously I'm speaking with companies asking what kind of data do you have what you train on real fine stuff for example the ang data is out there in public and people are actually using that as real data from a

08:04 company how a company works like and the 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.

08:29 Now, you can do that when you go to customers. So, you 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 at your initial interaction. So I think 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 mascul think about it contains sensitive

09:03 information like PII financial information so on so forth and actually be able to build real world stuff on top of that. Yeah, and Google obviously is uh it's not it's not they're in this travel uh space for a while, right? They they want this type of data. Uh they're already monetizing it. This allows them to uh understand, train it, understand it, monetize it even further and it's a unique situation, right?

09:25 That uh 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 uh who have existing data want to be able to unlock that existing data as well. what what sort of tooling are you all building at Eon to allow people to make use of their data for AI applications?

09:42 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 do you need new tools? Isn't it sold already so many great companies throughout the years and everyone understand data is important?

10:02 So to put it this way um back in the days every data team could find their own data decide what project do they have and you know get data do something with that very tactical they were using I don't know some great companies FAR 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 you're a data team leader

10:40 in in some company and you are based in San Francisco or we're now here uh in New York and both of us 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 sh and the board and the shareholders They understand that AI is real. >> So they're coming to you and they tell you a lad we have a lot of data in the organization.

11:11 We now realize data is new oil. We can actually activate it with the new tools that we have today. We 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 I know all of those new cool things that coming out every day in Silicon Valley.

11:37 I can just leverage them. The problem is where's the data? And so you come to us and we are business unit leaders. If you even know us, maybe you don't, but let's say that you find somehow got to me. I'm a a leader of a business unit. I have a a data probably and somehow you convince me to give me access to my data. Now I don't know what data do I have.

12:02 I have a lot of people working for me. They have data in multiple systems for the last 20 years. Some of them system that no one really understands where they contains production data because sensitive information you know there's always this server that no one knows what it's doing but connected to the to the power whether virtual or physically that everyone's afraid to turn off because we don't know what's in there.

12:27 So we had we had all all of all of that and let's say that somehow I know what's in there. Now I need to bring engineers and and compromise maybe security and compliance and and uh production up time and to extract the data just to give it to you and store it 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.

12:56 You 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 train to be used for training or post training by you. So we aton 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 understand what it is classify it map it understand context layer

13:32 on top of that as build a semantic layer and then be able to continuously bring all the data from me that is relevant without compromising production without compromising security compliance we're actually keeping audit and because data classify. I know that I'm not accidentally going to share with you sensitive information that you shouldn't have eventually in your data.

13:59 We can do it in a very costefficient and performant way. So you can actually do it from all over the place, bring it to you and actually use. >> 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.

14:22 And then third is it sounds like all this can then be exposed into AI models for sort of their uses or applications. And >> yeah and the key and the key point to that 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 in uh different forms but it's uh locked.

14:40 It's not accessible and usually it's very very expensive. Right? So we're able to take what customers already have, convert it into this new data foundation format that's much more stored much more efficiently and provide the mapping, classification, access control, um, and connect it into the AI workflows. >> How 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 escape sandboxes and do all sorts of things and you know there may be broader things of

15:10 foot in terms of why that's happening beyond just the agent capabilities like who knows how these things are set up or configured or you know um sometimes it's a little bit uncertain whether you know there's there's that much uh uh how people are approaching these things but you know fundamentally there's a lot of discussion of like AI security um how do you think about that in the context of the enterprise stack what people should do or not do, how CISO should be thinking about all this.

15:34 >> Yeah. So, up until now, the concerns came from uh human threats, right? So, this is uh uh not new where customers would come to us and say, "Hey, uh we were exposed by this uh ransomware attack." So, during our time at AWS, it was a very large customer that was impacted by ransomware. We we thought that they were completely protected using our technology, the uh disaster recovery service that we managed there.

15:58 And we learned unfortunately that the customer thought that they were protected. They weren't protected because they didn't uh map and classify and tag their resources properly. So it wasn't protected. And so 60% of the environment was uh was exposed by ransomware. And that's one of the reasons why we decided to launch Eon and solve that uh pain point around human threats such as ransomware.

16:18 So being able to detect when that happens, look for uh irregular right patterns and entropy changes and things like that, protect against it, and then also allow customers to uh uh 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 nonhuman actors from uh AI agents that essentially have legitimate access to the environment with legitimate permissions into such and such databases.

16:44 and all of a sudden and this now happens very rapidly uh a table is all of a sudden dropped. Uh 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. Yeah, something I I I noted is that like six months ago, no, no one would even discuss with me.

17:08 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 per to to the the that person who speaking with me, which is crazy. You see it all over the place. You seem real fear from e I no longer decide what really running on my data. I don't know longer understand.

17:39 I need to be prepared for both external threats because you know all the new muggers make it much easier for attackers to h come to me and and and attack me. but also from the inside with agents I actually approved running in my environment. >> So 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.

18:10 It's very weird situation today. >> Yeah. How do you think about the broader um enterprise stack and agents? So you know the current stack really evolved around people or humans asking very defined analytical questions. So we have warehouses, we have dashboards, we have the ETL pipelines, we have BI and agents may have may behave differently and more dynamically.

18:36 They may be able to reason over much larger um sets of data. They may have access to SAP SAS apps and historical data and a variety of other things and then act and so what what do you think changes in terms of how you store access interact with data in the context of like the agentic world or what what else do you think needs changed 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 figure out what they have going on in the world because

19:05 first coding agent has started to write most of the code that's running in the wall. 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 impossible task. So many actors inside the organization when it's so very hard for a human to understand the the the chain of responsibility and this is a part of what you're seeing in proliferation of of cyber security companies.

19:38 how many cyber security companies you see in in NHI in non identity right now an infinite amount and there's a reason for that it became number one number two problem right now in addition to that second thing is endpoint you see endpoint security which looked like it solved them there so many great companies around it and just you know a few a few years ago when endpoint was a completely different problems with TVRS Now uh everything that's happening you see people are running agents today on on the laptops and the

20:13 agents sometimes connected to other networks and it connect they are connected to uh think on open claw connected to h to to your WhatsApp but also to your internal network and also to other applications and you see it's very hard for the for the VP of IT is for the CIOS to understand what should they do on the one hand they want to they are being pushed pushed by the board by the CEO enable AI in my organization now don't block me you can't block me on the other hand it's so scary I mean every person don't even think

20:56 of technical people think of the non-technical people building something with you know one let's say a lovable or or any other software that you for 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 all of those new cool things. So maybe their agent that they are building are using other agents and they're not technical to even understand what it means.

21:26 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 handling 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 a big problem.

21:51 It's a good thing and bad thing that everyone inside organization can become builders. Whether you're a social media manager, whether you're a a finance person, whether you're in in in in legal or finance. >> Mhm. >> So, it's amazing, but it's also we live in very interesting times in that perspective. >> How much of the um existing data infrastructure do you think survives all this?

22:15 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 compelling 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.

22:40 So think about what happens now. Gonen goes downstairs after recording this podcast and he really wants coffee. So go to the store and and buys 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.

23:06 someone else at some point takes this data and process in 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 context all the data organization because you could only do with the data things you really intended to do to begin with.

23:31 So you had a single purpose in your mind when acting on the data. Mhm. Mhm. >> Today it's very different. Today you understand that you can collect if you're able to smartly collect and clean all your data and make sure you store in efficient manner and if you can activate that efficiently, you can let a team go wild with all the data that they have.

23:53 And the more data that they have and the more high quality data that they have and the more context on that data that they have the team handling 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 in the organization have who has a database of all the people in New York who love pizza.

24:19 They don't know they can find a a a list of all the people in New York who love birth burgers on pizza because not they didn't work together. Now if you use it for poster and use it for a for the new capabilities you can actually do wonders with that. You can actually start asking intelligent questions your data intelligent questions. You can start using that for your own purposes and just something that you couldn't do before.

24:45 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 continuously 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.

25:08 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 for multiple locations, clean the noise and make sure all of these data that's been created is actually usable. >> Mhm. >> And it doesn't apply with the old tools that were very some of them were incredible.

25:31 Five was an incredible company, DBT and so on so forth, but very specific tools for that purpose. Uh so so this creates a a very interesting brave new world. You've seen companies like data bricks, you know, one of the most incredible companies on the planet in my opinion and looking at I have more and more data coming in. >> Mhm. >> I don't necessarily know where it is.

25:59 I'll help you catalog the data and make use of that, but it's an after effect. >> You already have the data. Now you need to process that. H 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 got we they want to take charge of how data is being used data is being created >> and it's completely different than how any

26:34 other people use that just three or five years ago. >> Yeah. 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.

27:00 Makes sense. >> And and you see every 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 sort of problem but then you need to access that you need to pay for that for storage and of course tokens and we're not in the time of token maxing anymore >> trying to go to actually getting value for every token that we have because comes more and more and more imper more and more and more expensive.

27:30 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 so it's very expensive very lucrative let's make it relatively as least expensive as you can have it. >> So I guess um you know the other thing that you guys have really lived through is the cloud transition.

27:56 So prior to Eon, you started a company called Cloud Endor that was acquired by AWS. And at AWS, you really saw that migration uh from onrem 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 what do you view as sort of the cloud era versus AI era and what are takeaways or lessons that you can apply across them?

28:26 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 endor to AWS, we uh supported similar large scale enterprise migrations with 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.

28:53 uh and after we sold to AWS, we did that as part of the application migration service. Uh but that's kind 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 h happening way faster and uh and customers are losing control to a point where that's becoming an inhibitor, right?

29:19 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 of understanding of what's happening and control. >> So it became so insane that 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.

29:43 Cloud is basically just someone else's computer, but who knows what it is. It's hard to explain to my grandmother about the cloud AI. Everyone understands AI. Everyone, everyone lived from the CHP moment when we all left what AI could do. Oh my god, this is incredible. So they getting pushed by by sea levels, by CEO, by the board, by the shareholders use AI for the business otherwise otherwise we're relevant.

30:18 So you see people doing it both for the value that you get from AI but also for the from the fear that you get from AI and you see new trends of for a first time in in a lot of in in in in many years you see how companies consume software in a brand new way what's happening with forward deployed engineers used to be something look like services palenteer were doing that.

30:46 No one really did understand what it means and now everyone's doing that. >> Now it seems that you come into a large legacy enterprise, they really want to adopt AI because they have to. The problem is they don't know how to do it. They understand that their processes are very long. They some takes a year or or two or more, but they need to have it now.

31:09 And the only way they can actually get it deployed and and and become AI much faster is by letting a a strong engineers who understand what they're doing and coming with the um tools that they've created in top Silicon Valley startups and sometimes larger companies to come and transform those organizations. And you see them shrinking sales cycles and you see companies growing really fast because of that.

31:43 You also see companies buying really fast especially the new companies uh buying using productled growth by in really really fast a AI infrastructure with which actually h helped to build agents because everyone now want to build agent. Now in the past I was arguing that for the majority of things PLG doesn't work especially for dev tools uh because the world is very fragmented people don't want to move so fast so forth now it became super hot looking companies like cognition for example which is you know incredible

32:23 company that were able to first go through a a PLG we use that that way in Eon and then through the FD motion telling going to banks and then will 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 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

33:00 example long lake that say instead of you adopting AI I know how to do it more efficiently. If I can buy the company and transform that into an AI company we can all win. we can attract arbitrage make 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

33:32 something is happening they understand the data is important they understand that their existing processes are somewhat e mundane and they 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 fasc fascinated evolution on >> what's going on right now in how how companies consume AI software, how conso companies transform into being more modern, how much we being pushed to do that.

34:05 And I think that eventually I know it's a very wild ride but I think everyone is going to go the world in my opinion is going to be better because of that. >> Amazing. Well, thank you so much for joining me today. Very interesting wide ranging conversation on data and AI. Really appreciate it. >> Thank you. Our pleasure. >> It was a pleasure. >> All right.

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