AI Writes Code.
Who Catches the Vulnerabilities?

What are vulnerability scanning tools?

Most teams discover their first serious vulnerability the hard way — a scanner fires after deployment, and suddenly a sprint gets hijacked by a triage meeting nobody planned. Tools like Nessus and QualysGuard catch network-level problems (open ports, outdated packages, misconfigurations), while Burp Suite and OWASP ZAP hammer web applications for injection flaws and broken authentication. Both categories do their job well, but they share a timing problem that frustrates developers: they find issues after code reaches a test environment, not while someone is still writing it.

Git AutoReview shifts vulnerability detection earlier. Instead of scanning a deployed application, it analyzes your pull request diff for SQL injection, XSS, hardcoded secrets, and OWASP Top 10 patterns while you review code in VS Code. This catches the same vulnerability classes that network scanners find — just weeks earlier in the development cycle, when fixes cost minutes instead of sprints.

What are SAST tools and how do they compare to AI code review?

The appeal of SAST is brute-force coverage — SonarQube ships over 6,500 rules across 30+ languages, and CodeQL traces data flow through your entire codebase with semantic queries. Snyk Code, Semgrep, Checkmarx, and Veracode each bring their own rule databases and CI/CD integrations. When a SAST scan runs, it checks everything against every rule it knows, which means zero false negatives on patterns the tool recognizes. That exhaustiveness is genuinely valuable for compliance audits and quarterly security reviews.

The trade-off is noise. Enterprise SAST deployments regularly generate hundreds of findings per scan, and teams struggle to triage them between sprint work. AI code review works differently — it reads the PR diff the way a senior engineer would, catching logic flaws, race conditions, and context-dependent bugs that no regex rule can express. The two approaches complement each other: SAST for exhaustive rule coverage, AI review for intelligent analysis of what changed.

Git AutoReview bridges the gap with 20+ built-in security rules (the SAST side) plus a specialized AI security pass that reasons about your code changes. Teams running SonarQube or Snyk for quarterly compliance audits add Git AutoReview for real-time, per-PR security feedback in VS Code — covering the gap between the last SAST scan and the next one.

What is static code analysis?

Every CI/CD pipeline already runs some form of static analysis — ESLint catches style issues, TypeScript catches type errors, and tools like Checkmarx catch security flaws. The difference between those layers matters: linters enforce conventions, type checkers prevent runtime crashes, and SAST scanners flag vulnerability patterns. Most teams stack all three without thinking about it, which is exactly the right approach because each one catches a different class of defect.

The challenge in 2026 is that AI coding assistants generate code faster than static analysis can keep up. Copilot and Cursor produce working code that passes linters and type checks while embedding subtle vulnerabilities — hardcoded tokens, missing input validation, insecure default configurations. Git AutoReview adds an AI layer on top of static analysis: it understands intent, not just syntax, and flags the security mistakes that rule-based tools miss because no one wrote a rule for that particular pattern yet.