Empirical Lands $12M to Replace Generic Cyber Models with Custom AI

When Ed Bellis helped build Kenna Security, the startup that brought data-driven thinking to vulnerability management, the biggest breakthrough was in their mindset: Prioritization was the future. More than a decade later, Bellis says that model still doesn’t go far enough. “Security is deeply contextual,” he wrote. “What’s high-risk for one organization might be irrelevant for another.” That’s the premise behind Empirical Security.

The Chicago-based startup, which just raised $12 million in seed funding led by Costanoa Ventures, with participation from DNX Ventures, Sixty Degree Capital, and several strategic angels, builds machine learning models that learn from a company’s actual security environment: its data, systems, and internal telemetry. Co-led by Bellis, Michael Roytman, and Jay Jacobs, all former Kenna Security leaders and the creators of the Exploit Prediction Scoring System (EPSS), Empirical says that local models can outperform the generic, one-size-fits-all systems that most companies rely on today.

“Today’s cyber attacks are custom-built using AI and your own infrastructure against you,” Roytman, now CTO of Empirical, said. “Defending with generic, one-size-fits-all models is a start, but only custom, localized models, trained on your data and environment, can close that gap.”

Bellis, who took over as CEO of Empirical, described the new venture as a continuation of the same mission he’s worked on for decades: using data, not assumptions, to drive security decisions. “The next frontier isn’t just more data, it’s more relevant data, more adaptive models, and more context-aware decisions,” he wrote in a company blog post announcing his move to Empirical.