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Empirical Security Announces $25M Series A to Defend Against AI-Accelerated Exposures

Insider Brief

  • Empirical Security, which builds predictive models for exposure management, has raised $25 million in Series A funding led by Brightmind Partners, bringing total funding to $37 million following earlier backing from Costanoa Ventures and Hyde Park Angels
  • The capital will accelerate two products: Foundation, a global model monitoring more than 18,000 exploited CVEs, and Radiant, a custom predictive engine fine-tuned to each organization’s environment; the company is led by Kenna Security veterans Ed Bellis, Michael Roytman, and Jay Jacobs, co-creator of the EPSS scoring system
  • The funding comes as Verizon’s 2026 Data Breach Investigations Report found vulnerability exploitation overtook stolen credentials as the leading initial access vector for breaches, accounting for 31 percent of confirmed breaches, up from 20 percent the year before

PRESS RELEASE — Empirical Security, the cybersecurity company that builds foundational and predictive models for exposure management, has announced $25 million in Series A funding led by Brightmind Partners. The round follows the company’s earlier financing from Costanoa Ventures, Hyde Park Angels (HPA), and others, bringing its total funding to $37 million. The new capital will accelerate the development and deployment of the company’s two flagship products: Foundation, a global model that helps organizations predict threats by monitoring over 18,000 exploited CVEs, and Radiant, a custom predictive engine built and fine-tuned for each organization to identify the threats most relevant to its unique environment.

As AI rapidly expands the scale and complexity of cyber threats, security teams face mounting pressure to predict and prioritize the potential exploits that matter most. Security leaders are frustrated with legacy exposure management tools that rely on generic risk models built from expert opinion and assumptions that are never validated against the real world. Empirical Security’s predictive models provide organizations with the intelligence needed to distinguish genuine risk from background noise, enabling lean security teams to make faster, more informed decisions. Empirical Security works with enterprises that can’t rely on guesswork to prioritize potential threats, particularly organizations in technology, healthcare, and financial services.

The company is led by a trio of proven executives who collectively invented the concept of risk-based vulnerability management and predictive intelligence. Co-founder and CEO Ed Bellis, who previously co-founded Kenna Security and served as CTO through its acquisition by Cisco, is joined by co-founder and CTO Michael Roytman, formerly Chief Data Scientist at Kenna Security. The third co-founder is Chief Data Scientist Jay Jacobs, co-creator of the Exploit Prediction Scoring System (EPSS), a vulnerability threat model trained and maintained by Empirical Security whose scores are published daily and free for anyone to use. Hundreds of companies and vendors such as Tenable, Qualys, Crowdstrike, Microsoft and Wiz have embedded EPSS across their systems. 

“I had unfinished business from my time building and selling Kenna Security,” said Ed Bellis, co-founder and CEO of Empirical Security. “We helped pioneer the category of risk-based vulnerability management, but it became clear that defending against AI-driven threats and the growing volume of potential exploits would require a fundamentally new approach. Today, we finally have the technology to give security teams predictive capabilities that weren’t possible before, and we came together to build that future.”

The need for that approach has never been greater. According to Verizon’s 2026 Data Breach Investigations Report (DBIR), which incorporated analysis from Empirical Security, vulnerability exploitation became the leading initial access vector for data breaches, surpassing stolen credentials for the first time. Exploited software vulnerabilities also accounted for 31% of confirmed breaches, up significantly from 20% the previous year.

Empirical Security’s models enable threat and vulnerability teams to dramatically increase their effectiveness without expanding headcount, providing organizations with transparent, data-driven insights to measure and manage cyber risk rather than relying on static or generic risk scores.

“Brightmind wants to work with visionary founders solving old problems with a generationally new approach,” said Stephen Ward, Founder and General Partner of Brightmind Partners. “Ed, Michael, and Jay checked all those boxes, and we’re excited to accelerate Empirical Security’s journey to market at a time when their prospects and customers are faced with the biggest crisis in the history of exposure management.”

Unlike traditional exposure management platforms that rely on the same generalized threat data across customers, Empirical Security builds custom predictive models enhanced by AI and tailored to each organization’s unique environment. The result is deeper forecasting, more actionable intelligence, and evidence-based risk analysis that empowers security teams to prioritize remediation efforts with confidence and justify their decisions to stakeholders.

To learn more about Empirical Security, visit www.empiricalsecurity.com. 

About Empirical Security
Empirical Security is a cybersecurity intelligence company that equips organizations with predictive exploit intelligence through custom-built AI and machine learning models. Empirical’s state-of-the-art approach to exposure management rapidly finetunes each customer deployment, leveraging curated, AI-mined, and customer-provided data. This approach delivers localized models offering advanced decision support tailored to each organization’s environment, risk threshold, and specific needs.

Starship PR for Empirical Security
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Featured image: Credit: Empirical Security

James Dargan
About the author
James Dargan

James Dargan is a writer and researcher at The AI Insider. His focus is on the AI startup ecosystem and he writes articles on the space that have a tone accessible to the average reader.

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