Traditional SAST tools produce volumes of findings, many of which are false positives. A hardcoded string that looks like an API key but is actually a test fixture. An SQL injection warning on a parameterized query. Security teams spend hours triaging findings that are not actually vulnerabilities.

The AI Approach

An LLM reviews security scanner findings with code context to assess whether each finding is a real vulnerability. It understands that a parameterized query is safe, that a string in a test file is not a leaked secret, and that an eval() call in a sandboxed environment has different risk than one in a web handler.

Three-Step Build

  1. Scan - Run your existing SAST tools (Semgrep, SonarQube, CodeQL) to produce raw findings.
  2. Triage - For each finding, extract the surrounding code context and send it to an LLM. Ask it to assess: is this a real vulnerability? What is the severity? Is there a mitigating factor in the surrounding code?
  3. Prioritize - Present verified findings sorted by severity and exploitability. Include the AI’s reasoning so security reviewers can quickly validate the assessment.

Where It Breaks

The AI may dismiss real vulnerabilities if the exploitation path is non-obvious. Security is a domain where false negatives (missed real vulnerabilities) are more dangerous than false positives. The AI should reduce noise, not replace human security review.

The Production Path

Integrate into CI as a post-scan triage step. Track the AI’s accuracy by comparing its assessments against human security reviewer decisions. Use disagreements as training data. Over time, the AI handles the obvious false positives automatically, freeing security engineers to focus on the findings that require expert judgment.