Rethinking Legacy Data Infrastructure with Eon Co-Founders Ofir Ehrlich and Gonen Stein
In a Nutshell
Eon built a cloud data foundation that maps, classifies, ingests, and exposes enterprise data for AI use while preserving security, compliance, and cost efficiency. The biggest shift is that AI agents—non-human actors with legitimate access—now pose the same threats as ransomware but at far higher velocity, forcing companies to assume breach and rebuild data infrastructure for dynamic, multi-agent workflows. Real-world enterprise data has become a strategic asset and acquisition target, turning historical “backup” data into high-value training fuel that differentiates companies in an otherwise level AI playing field.
These notes were generated by AI and may contain inaccuracies.
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. 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. 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 Ofir Ehrlich and Gonen 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.
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. Then 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.
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 data becomes the most important thing that an organization have when you can think about it models compute everything is relatively ephemeral almost zero switching cost and those are infra important part of the infrastructure for 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.
Just two days ago, you saw Google buy something from the bankrupt 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 perspective. They're using that to train models.
The rumor too is that the other bidder on that data set was Mercury, 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.
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. Are you willing to sell your data? I hear it all over and it seems that's going to be 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 see a data that was accrued throughout the years by companies which was usually like tapes. It was usually 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 flatten the playing grounds. It seems that everyone can start even large and small companies basically have 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.
Because of post-training and reinforcement learning and you know there's companies like applied 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. If you're trying to build agents today and you can't just build them a lab need to train them on new data on some training data and it's very hard to find very good data sets.
You see that Harvard just released 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 data can be used both as an airline company but also as a large enterprise as a place where lots of people work a lot of the hierarchy middle management top management and workers working together and you know if you're looking at what other public data sets do you have out there there aren't a lot of those there's 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 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. 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 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 mask think about it contains sensitive information like PII financial information so on so forth and actually be able to build real world stuff on top of that.
Let's go back from the problem statement and why there are so many tools for data and processing. 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 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 Fivetran 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 in some company and you are based in San Francisco or we're now here 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 ChatGPT so the CEO 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. 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 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 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 leader of a business unit. I have 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. 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 power whether virtual or physically that everyone's afraid to turn off because we don't know what's in there.
So we had 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 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. 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 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 understand what it is classify it map it understand context layer 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. We 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.
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. 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 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, convert it into this new data foundation format that's much more stored much more efficiently and provide the mapping, classification, access control, and connect it into the AI workflows.
Up until now, the concerns came from human threats, right? So, this is not new 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. So it wasn't protected. And so 60% of the environment 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 nonhuman actors from AI agents that essentially have legitimate access to the environment with legitimate permissions into such and such databases. and 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.
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 speaking with me, which is crazy. You see it all over the place. You seem real fear from I no longer decide what really running on my data. I don't know longer understand. I need to be prepared for both external threats because you know all the new muggers 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.
So it's a 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 very weird situation today.
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 behave differently and more dynamically. They may be able to reason over much larger 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 do you think changes in terms of how you store access interact with data in the context of like the agentic world or 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 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 chain of responsibility and this is a part of what you're seeing in proliferation of cyber security companies. how many cyber security companies you see 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 years ago when endpoint was a completely different problems with TVRS Now everything that's happening you see people are running agents today on the laptops and the agents sometimes connected to other networks and it connect they are connected to think on open claw connected to your WhatsApp but also to your internal network and also to other applications.
It's very hard 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 of technical people think of the non-technical people building something with you know one let's say a lovable 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 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. 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 big problem. 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 finance 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.
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. So think about what happens now. Gonen goes downstairs after recording this podcast and he really wants coffee. So go to the store and buys coffee and he puts on his credit card. Now there's a transaction and this is written in some database somewhere.
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 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. 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'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. 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. They don't know they can find a list of all the people in New York who love both burgers on pizza because not they didn't work together. Now if you use it for poster and use it 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.
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. So there's 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.
It doesn't apply with the old tools that were very some of them were incredible. Fivetran was an incredible company, DBT and so on so forth, but very specific tools for that purpose. So this creates 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. I don't necessarily know where it is. I'll help you catalog the data and make
Organizations are actively reinventing themselves as data generation increasingly comes from agents rather than traditional sources. Companies are building their own agents and databases to take control of how data is created and used, representing a fundamental shift from how data was managed three to five years ago.
The goal is to enable a culture of builders and agents by automatically helping teams understand existing data and ingest it without building manual pipelines for each new application. This approach also helps maintain control over newly created data.
While having large amounts of data is valuable, the challenge lies in scattered data that requires payment for storage and token usage. Organizations have moved beyond token maxing to focusing on getting actual value from every token consumed, as costs continue to rise significantly.
The strategy involves being strategic about token usage rather than avoiding large expenses entirely, but ensuring the value received justifies the investment.
The current AI infrastructure shift mirrors the earlier cloud transition but occurs at much faster speed and scale. Previous experience with CloudEndure, which was acquired by AWS, involved supporting large-scale enterprise migrations across hyperscalers including Azure and GCP, with products integrated into consoles for moving thousands to hundreds of thousands of servers.
The key difference is that AI transformations happen significantly faster than cloud migrations, causing customers to lose control to the point where it becomes an inhibitor rather than an enabler. Organizations are pausing implementations due to concerns about system failures, data leaks, and IP exposure.
Cloud computing remains abstract and difficult to explain, essentially meaning someone else's computer. In contrast, AI has universal recognition, with widespread understanding emerging from the ChatGPT moment when people first experienced its capabilities.
This creates dual motivation for AI adoption: the value gained from AI implementation combined with fear of becoming irrelevant if competitors adopt it first. Pressure comes from all levels including C-suite executives, boards, and shareholders.
Companies are consuming software differently than before, with forward deployed engineers becoming standard practice. This approach, previously associated mainly with companies like Palantir, is now widespread across the industry.
Legacy enterprises want to adopt AI but face challenges with lengthy internal processes that can take a year or two. The solution involves bringing experienced engineers from Silicon Valley startups and larger companies with pre-built tools to accelerate transformation.
Companies are purchasing AI infrastructure rapidly, particularly newer organizations using product-led growth strategies. This shift contradicts previous assumptions that product-led growth doesn't work for developer tools due to fragmented markets and slow adoption rates.
Companies like Cognition demonstrate successful product-led growth approaches, which Eon also employs. The model then transitions to forward deployed motion, where specialized engineers handle implementation work that organizations prefer not to manage internally.
Some companies are taking a different approach to AI adoption by acquiring organizations rather than implementing AI themselves. Long Lake exemplifies this strategy, purchasing companies and transforming them into AI-native operations to achieve higher margins and operational efficiency.
This represents a radical new path for companies to leverage AI capabilities through acquisition rather than internal transformation.
Most companies remain at the beginning of their AI journey despite understanding that significant changes are occurring. Organizations recognize data importance and acknowledge that existing processes are often mundane, yet many find the transformation process intimidating.
The revolution in AI adoption has only just begun, with the majority of companies still needing to implement meaningful AI capabilities.
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