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

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 nonhuman 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 nontechnical people. They're not even aware for things like security or compliance or who is gonna 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 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. Today, I know priors, we're joined by Ofer Ehrlich and Gunen 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. Afiyo Ghonen, thank you so much for joining me on the prize 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. I think there's a whole stack that's built on top of different types of datasets. And so maybe we can start with what you all do, and then I think we'll kinda walk through, like, how the world is shifting relative to to the enterprise data stack. Yeah. Sure. So, what we do at at a high level is we've created a a 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 variety of sources. Yeah. And my sense is, I mean, …