AI SAST: Code Security for the Agentic SDLC

AI now writes a large share of production code; almost half of companies have codebases that are at least 50% AI-generated. Security is not keeping up. In benchmark testing, only 10.5% of solutions from an AI coding agent came back secure. The tools meant to catch security flaws fail in opposite directions. Pattern-based SAST is noisy, roughly 69% false positives in NIST's testing of C/C++ tools (2018), and it misses broken access control and business logic flaws. Frontier models are the reverse: precise, but they examine only a small slice of the relevant code. This whitepaper explains how Endor Labs AI SAST closes that gap by pairing deterministic program analysis with agentic reasoning. We cover: - How it holds up on a ground-truth benchmark against 4 traditional SAST tools and 2 frontier models: 192 real vulnerabilities found, more than double any other tool - How the analysis pipeline works, from the code graph through detection and triage to a proposed fix, and why agents query a structured map of your code instead of reading raw text - How the system controls for LLM non-determinism, so findings keep a stable identity from scan to scan, with evidence that holds up in an audit Read the full whitepaper → What's next? When you're ready to take the next step in securing your software supply chain, here are 3 ways Endor Labs can help: