Empirical Security Raises $25 Million in Series A Funding

The startup will use the investment to accelerate the development of its threat prediction and discovery products. The post Empirical Security Raises $25 Million in Series A Funding appeared first on SecurityWeek .

The article reports that Empirical Security, a cybersecurity startup building threat prediction and discovery products using AI-driven, predictive exposure management models, has raised $25 million in Series A funding to accelerate product development and growth.[1][2] This funding expands the use of machine-learning models trained on exploitation data and enterprise telemetry to help organizations prioritize vulnerabilities and threats.[2] From a RealGround perspective, increased reliance on Empirical Security’s AI models for risk scoring and prioritization introduces AI supply chain considerations: downstream enterprises will depend on the integrity, training data quality, and update processes of a third-party AI system embedded in their security workflows. Organizations should treat Empirical’s platform as a critical AI dependency, requiring SBOM-style transparency, model update governance, and contractual controls around data handling and model behavior to avoid hidden systemic risk.