AI Code Review vs SAST: What Each Catches and How to Use Both

SAST and AI-based code review use complementary methods: SAST applies deterministic pattern matching and source-to-sink taint tracking to reliably identify known vulnerability classes such as injection, path traversal, cross-site scripting, and weak cryptography, while LLM reviewers interpret code changes and surrounding context to identify missing authorization, insecure design, IDOR, and business-logic flaws that rules may not express. SAST produces repeatable, auditable findings with traces, whereas AI output can vary based on model behavior and available context and may provide persuasive but unverified narrative explanations. Both approaches can generate false positives for different reasons, including unrecognized custom sanitizers in SAST and unsupported assumptions or missed paths in AI review. The text argues that combining deterministic analysis with AI reasoning and evidence-based triage provides broader coverage, using confirmed, false-positive, and unknown verdicts to distinguish validated findings from uncertain suggestions.

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