Incident Forensics When the Code Was Written by AI
Post-incident forensics change when no human wrote the code. Here is how to trace, analyze, and remediate vulnerabilities in autonomously generated systems.
When a security incident occurs in a traditionally developed system, the forensic process traces the vulnerability to a specific commit, a specific developer, and ideally a specific decision. When the code was generated by an autonomous system, the forensic chain is different. There is no developer to interview. The decision trail is in the governance log, and the root cause may be in the generation policy rather than the generated code.
The forensic trail in autonomous delivery
In a well-governed autonomous delivery system, the forensic trail is actually more complete than in traditional development. Every generation action, policy evaluation, review decision, and deployment step is logged with full context. The challenge is not finding the trail. It is understanding how the generation policies, model behavior, and input context combined to produce the vulnerable output.
- Trace the vulnerability to the specific generation action and the policy version active at the time
- Analyze whether the vulnerability was a policy gap, a model limitation, or an edge case in the input
- Search for similar patterns across all generated code to identify systemic risk
- Update generation policies to prevent the vulnerability class from recurring
- Generate remediation patches for all instances of the vulnerability pattern across the codebase
When AI-generated code has a vulnerability, the fix is not a patch. It is a policy update that prevents the entire class of vulnerabilities from being generated again. Every incident should make the system permanently smarter.
See governed autonomy in action
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