Empirical Security provides a data‑driven exposure management platform that combines a global exploitation model—trained on telemetry from over 17,000 exploited CVEs—with a local model built on each organization’s asset inventory, patching cadence, controls, and network topology. The platform delivers calibrated probabilities of exploitation for every vulnerability, accessible via a web UI, REST API, or AI‑powered agent, enabling security teams to prioritize remediation on findings most likely to be weaponized in their specific environment.
Funding
$11.9M raised to dateRaised to date based on public sources. This may differ from the amount the company actually raised and is based only on what is publicly available on the internet.
5OCVFounders
Product
Problem
Security teams struggle to prioritize vulnerabilities because traditional severity scores and generic exploit predictions do not reflect the actual risk in their specific environments, leading to wasted effort and missed attacks.
Solution
Empirical Security offers a data‑driven exposure management platform that combines a global exploitation model—trained on telemetry from over 17,000 exploited CVEs—with a local model trained on each organization’s own asset inventory, patching cadence, controls, and network topology. The platform delivers calibrated probabilities of exploitation for every finding, enabling teams to focus remediation on the vulnerabilities most likely to be weaponized in their environment. Real‑time exploitation telemetry, hourly updates, and millions of correlated malware hashes keep the predictions current. Users can access the insights through a web UI, REST API, or an AI‑powered agent that automates triage, generates remediation plans, and drafts exception requests. The solution also provides full model transparency, allowing analysts to inspect the data points driving each score and to validate decisions with concrete exploitation evidence.
Target Audience
Primary customers are enterprise security teams responsible for vulnerability management, threat intelligence, application security, and cloud security, including SOC analysts, vulnerability managers, and risk officers.
Features
- Global model trained on ~2 million exploitation events across 17,000+ CVEs, updated hourly with near‑real‑time telemetry
- Local model that ingests an organization’s scan results, asset metadata, compensating controls, patching velocity, and network topology to produce environment‑specific exploit probabilities
- Unified platform UI and API for querying scores, visualizing exploitation indicators, and integrating with ticketing, CMDB, EDR, and cloud security tools
- AI agent that can triage findings, suggest patches, draft exception requests, and surface emerging threats based on model output
- Full model transparency: every feature contributing to a score is exposed for audit and confidence building
- Coverage, efficiency, and effort metrics that quantify how well prioritized vulnerabilities align with observed exploitation activity
- Integration of millions of malware hash mappings and 1,400+ OSINT sources to enrich exploitation context