Rethinking Legacy Data Infrastructure with Eon Co-Founders Ofir Ehrlich and Gonen Stein
Ofir Ehrlich and Gonen Stein, co-founders of Eon, discuss how AI and agents are fundamentally transforming enterprise data infrastructure. They explain that data has become the most valuable asset for organizations, and new tools are needed to securely map, classify, and activate this data for AI applications while managing risks from autonomous agents with legitimate system access.
Summary
Eon is a cloud-based data foundation platform designed for the AI era that helps enterprises map, classify, and ingest data from multiple sources, then make it accessible for AI models and LLMs while maintaining security and compliance. The co-founders trace a significant shift in how companies view data—from static assets managed by siloed business units to dynamic resources that can be leveraged across the organization for competitive advantage. They point to Google's $10 million acquisition of Spirit Airlines' data from bankruptcy as evidence that data itself has become a directly tradeable asset valued for training AI models.
A core problem the founders identify is that most enterprises don't know what data they have or where it resides across their organization. Business unit leaders own data in disparate systems but face conflicting incentives—they want to enable AI initiatives while protecting production systems, security, and compliance. Eon solves this by discovering data without disrupting operations, classifying sensitive information, and providing controlled access through a unified foundation.
The co-founders highlight a new security paradigm: threats are no longer primarily from external human actors but from non-human agents with legitimate access and permissions. These agents can operate across organizational boundaries, activate other agents, and potentially expose sensitive data at extreme velocity. Traditional ransomware detection methodologies apply, but the speed of agent-driven threats is unprecedented. They note that six months ago no one discussed this risk, but now nearly every enterprise leader either fears or has personally experienced data exposure through agent actions.
Regarding the broader infrastructure shift, the founders argue that the existing data stack built for human-driven analytics—ETL pipelines, warehouses, dashboards, BI tools—is insufficient for an agentic world. Agents access data dynamically, reason over larger datasets, and invoke other agents in ways that are difficult for humans to track or govern. This creates a paradox: organizations need to enable builders and agents to unlock data value, but simultaneously maintain visibility and control. The founders predict more dashboards will be needed, not fewer, as humans struggle to understand chains of non-human identities and their actions.
They draw parallels to the cloud migration era they experienced at CloudEndure (acquired by AWS), noting that the current AI transformation is happening far faster and with greater loss of control. Unlike the cloud, which is abstract, AI resonates immediately with C-suite executives who push for rapid adoption out of both opportunity and fear of irrelevance. They observe new deployment patterns including forward-deployed engineers from startups transforming legacy enterprises, product-led growth strategies for AI infrastructure, and strategic acquisitions of companies specifically to transform them into AI-native businesses.
About this episode
Google’s purchase of Spirit Airlines’ data out of bankruptcy signaled a shift in how the tech world values real-world datasets. Although compute and models get much of the attention, in this landscape, it’s data that is a company’s protective moat. Eon CEO / Co-Founder Ofir Ehrlich and President / Co-Founder Gonen Stein join Elad Gil to talk about how Eon is redefining cloud backup into a secure data foundation designed to power and protect enterprise AI. Ofir and Gonen discuss why historical enterprise data is in demand by AI labs, and how Eon facilitates access to scattered and locked data across business units through providing the mapping, classification, and access controls needed to connect it into AI workflows. They also explore how traditional ransomware defenses must now protect against rogue AI agents with legitimate system permissions, concerns around the influx of autonomous agents and non-human identities, and the implications for the breakneck speed of AI adoption compared to the slowness of the cloud era. Sign up for new podcasts every week. Email feedback to [email protected] Follow us on Twitter: @NoPriorsPod | @Saranormous | @EladGil | @Eon_io_ | @OfirEhrlich Chapters: 00:00 – Cold Open Trailer 00:59 – Ofir Ehrlich and Gonen Stein Introduction 01:27 – What Eon Does 02:41 – Data as Moat 06:43 – Training Agents with Good Data 09:39 – Data is the New Oil 15:00 – Autonomous Security Threats 18:15 – How Agents Change the Enterprise Stack 22:11 – Re-imagining Data Infrastructure 27:52 – Cloud vs. AI Era Shift 30:26 – How AI is Changing Companies 34:31 – Conclusion
Key Insights
- Google's acquisition of Spirit Airlines' data for $10 million demonstrates that historical enterprise data has become a directly tradeable commodity for training AI models, with multiple tech companies now bidding on bankrupt company datasets.
- The founders argue that most organizations already possess valuable data but it remains locked and inaccessible across siloed business units, creating a gap between what data exists and what can be activated for AI applications.
- Non-human agent threats now pose equal or greater risk than ransomware from human attackers, with agents exploiting legitimate system permissions to cause data exposure or deletion at unprecedented velocity—a problem that was not discussed six months ago but now affects nearly every enterprise leader.
- Traditional enterprise data tools (ETL, dashboards, BI platforms) were built for humans asking predefined analytical questions and are insufficient for agents that dynamically reason over data, invoke other agents, and operate across organizational boundaries.
- The agentic transformation is occurring faster and with greater loss of control compared to the cloud migration era because AI is universally understood and directly requested by C-suite executives out of both opportunity seeking and fear of competitive irrelevance.
- Non-technical employees using low-code tools are creating agents that access company data without understanding security implications, creating uncontrolled actors within organizations that are not bound by corporate governance rules.
- Forward-deployed engineers from Silicon Valley startups are becoming a new software deployment pattern, rapidly transforming legacy enterprises into AI-native organizations by bringing toolkits and methodologies that compress multi-year adoption timelines into months.
- The founders predict that enterprise visibility requirements will necessitate more dashboards, not fewer, as humans attempt to track increasingly complex chains of non-human identities and agent-to-agent invocations across organizational systems.
Topics
Transcript
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's 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…
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