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AI for Security Auditing

5 min read
AppsecPentestSecurity Arch

Appsec

Use AI for SAST-style review and CWE mapping. Verify every finding. AI misses context-specific vulnerabilities.

Pentest

AI can suggest attack vectors and draft exploit PoCs. Never run untrusted code. Verify before testing.

AI for Security Auditing

TL;DR

  • AI can help with code review, vulnerability classification, and threat model drafting.
  • It will miss context-specific and novel vulnerabilities. Never rely on it as the only reviewer.
  • Use AI to broaden coverage. You own the final assessment.

Security work requires skepticism. AI is a useful assistant — but it's not your penetration tester or compliance auditor.

Code Review for Security

What AI can do:

  • Flag common patterns: SQL injection, XSS, insecure deserialization, hardcoded secrets.
  • Map findings to CWE/OWASP categories.
  • Suggest fixes: parameterized queries, output encoding, etc.

What AI misses:

  • Business logic flaws (e.g., "can I escalate by changing this ID?").
  • Context: "This looks like injection" when it's actually a controlled admin-only input.
  • Novel or chained vulnerabilities.
  • Compliance nuances (PCI, HIPAA, etc.) — AI may oversimplify.

Workflow: Run AI review → treat as a first pass → you validate and deepen. Add your domain knowledge.

Threat Modeling

What AI can do:

  • Generate STRIDE-style threat lists from a high-level system description.
  • Suggest mitigations for common threats.
  • Draft threat model docs from a component diagram.

What AI can't do:

  • Know your assets, adversaries, and risk appetite.
  • Account for organizational and deployment context.
  • Prioritize based on your actual risk.

Workflow: You describe the system → AI suggests threats → you refine, validate, and own the final model.

Vulnerability Assessment

What AI can do:

  • Explain CVEs and suggest remediation.
  • Correlate scan results with known issues.
  • Draft vulnerability reports and recommendations.

What to watch:

  • AI can misclassify or conflate vulnerabilities.
  • Verify CVE details and patch status — models can be stale.
  • Never paste sensitive scan output (internal IPs, credentials) into public AI.

Red Team / Pentest Support

What AI can do:

  • Suggest attack vectors from a system description.
  • Draft exploit PoCs (conceptually) — useful for understanding, not for running blindly.
  • Summarize techniques from writeups and docs.

Critical caution: Never run AI-generated exploit code without understanding it. Never test against systems you're not authorized to test. AI doesn't know your rules of engagement.

You run a SAST tool. You get 200 findings. You manually triage. Half are false positives. You miss the business logic flaw that allows privilege escalation. 3 days of work. Still uncertain.

Click "AI first pass → you validate and deepen" to see the difference →

Quick Check

AI flags a potential SQL injection in your code review. What should you do?

Do This Next

  1. Run AI security review on a small module you know well. Compare its findings to what you'd flag. Note gaps.
  2. Use AI to draft a threat model for one system. Then refine it with your org's context. See how much you add.