Code analysis that reasons like a security engineer
How AI SAST works
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Your Tools, Your Languages
— All Secured
FAQs
Rule-based SAST matches patterns and can't tell whether user-controlled data actually reaches a vulnerable sink. AI SAST combines pattern matching, dataflow analysis, and multi-pass LLM reasoning on Endor Labs' code context graph to reason about how code behaves.
A model only reasons about the code it reads, so pointed at a repo on its own it covers only a narrow slice. On a large Java codebase, the security-relevant code those models examined dropped below 10%. AI SAST uses program analysis to pre-compute what needs review, so coverage doesn't compete with cost.
Yes. It traces data across architectural layers to detect IDOR, broken access control, insecure design, and fail-open authentication, categories rule-based SAST has historically missed.
AI SAST plugs into existing CI/CD pipelines and Endor Labs' policy engine, and surfaces findings with evidence and engineering-ready context, so security insights meet developers where they already work instead of in a separate tool.
AI SAST reasons over Endor Labs' code context graph instead of pushing your whole codebase through an LLM on every run. That's the same architecture that makes our agent tooling efficient: deterministic evidence in, less reconnaissance burned. It plugs into existing CI/CD pipelines without the runaway token costs of repo-crawling approaches.
Yes. OPA-based policies let you suppress false positives, block only on high-confidence findings, or route findings by team.