Empirical Security Raises $25M to Accelerate Predictive Security

The big picture: Empirical Security, a cybersecurity company building foundational and predictive models for exposure management, announced a $25 million Series A funding round led by Brightmind Partners. This brings their total funding to $37 million, aimed at accelerating the development and deployment of their flagship products, Foundation and Radiant. The company’s platform helps organizations predict threats by monitoring over 18,000 exploited CVEs and provides custom predictive engines. Learn more at www.empiricalsecurity.com.

Why it matters:

• Escalating Threats: AI is rapidly increasing the scale and complexity of cyber threats, putting immense pressure on security teams to accurately predict and prioritize potential exploits.
• Legacy Limitations: Traditional exposure management tools often rely on generic risk models built from expert opinions and assumptions, which are not consistently validated against real-world threat landscapes.
• Breach Vector Shift: Vulnerability exploitation has become the leading initial access vector for data breaches, surpassing stolen credentials for the first time, according to Verizon’s 2026 Data Breach Investigations Report.

How it works:

• Dual Product Strategy: Empirical Security develops Foundation, a global model monitoring over 18,000 exploited CVEs, and Radiant, a custom predictive engine fine-tuned for each organization’s unique environment.
• AI-Enhanced Customization: Unlike generic platforms, Empirical Security builds custom predictive models enhanced by AI, providing deeper forecasting and more actionable intelligence tailored to specific organizational needs.
• Data-Driven Prioritization: Their models offer transparent, data-driven insights to measure and manage cyber risk, enabling lean security teams to dramatically increase effectiveness and justify remediation decisions without expanding headcount.

The catch: The cybersecurity market is highly competitive, with numerous established players and emerging startups offering exposure management and vulnerability assessment tools. Empirical Security’s reliance on custom models, while a strength, could present scalability challenges in deployment and maintenance across a diverse client base, potentially increasing operational overhead compared to more standardized solutions. Furthermore, the effectiveness of predictive models is constantly challenged by the rapid evolution of AI-driven attack vectors, requiring continuous adaptation and validation to maintain accuracy.