How Endor Labs AI SAST Learns What Matters to Your Organization

Endor Labs presents its AI SAST as a security scanner that combines whole-codebase program analysis, semantic indexing, and specialized AI agents to identify and prioritize vulnerabilities, claiming benchmark results of 192 verified vulnerabilities across eight projects and more true positives than competing tools and models. Its approach incorporates context beyond source code, automatically analyzing Infrastructure as Code files to understand deployment topology and repository documentation such as AGENTS.md or CLAUDE.md to identify authentication, authorization, and intended application behavior. Teams can further improve results through finding-level feedback and natural-language architecture or threat-model context, which the system verifies against code and categorizes into project context, vulnerabilities to prioritize, and accepted false-positive patterns. Examples describe how external authorization systems, mandated authentication libraries, and WAF protections can help the scanner suppress irrelevant alerts or adjust severity while retaining visibility into meaningful risks.

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