Your security team can't review every pull request by hand. Static analysis tools like Semgrep and Bandit catch known patterns, but they miss logic flaws, insecure data flows, and context-dependent vulnerabilities. Layering a language model on top of those tools — using LLM reasoning to triage and explain findings — bridges that gap without replacing what already works.
Why Static Analysis Alone Falls Short
Traditional SAST tools operate on AST patterns and regex. They're fast, deterministic, and great for known issues — hardcoded secrets, obvious SQL injections, unvalidated inputs. But they fail at:
Business logic flaws — a function that looks safe in isolation but is dangerous in context
Chained vulnerabilities — where vuln A + vuln B = full compromise
