AI code review deserves a decision gate, not blind trust
Treat the AI output as a signal, not as approval
The practical choice is to place AI code review alongside existing tests, scanners, and peer review, with a human decision gate before a finding affects a ticket, block, or release decision. Assign a reviewer to every alert to determine the context, severity, and follow-up: close, fix, escalate, or record it as a test. NIST cites maintaining histories and audit logs as information AI actors can use to assess possible sources of error, bias, or vulnerability. That supports an auditable workflow, but does not prove that an AI code review tool accurately detects vulnerabilities.
Ask for auditable evidence before scheduling the work
Only allow a finding to proceed when the reviewer can document the affected change, the assumption, a reproduction step, and a validation test. This prevents a persuasively worded AI alert from automatically receiving the same status as a demonstrable finding. The European Commission cites activity logging for result traceability and detailed documentation in its description of obligations for high-risk AI systems before they are placed on the market. That passage does not classify an internal code review tool as a high-risk system; here, it serves solely as a design guideline for traceability.
Run one decision log for four weeks and assess the results
Use a fixed trial period for one repository and compare the outcomes with existing checks. For each finding, record the commit, review version used, reproduction step, human owner, triage decision, validation test, lead time, and review cost. This lets the team see which signals lead to validated improvements, which turn out to be noise, and how much review time the layer requires. The supplied passages describe general AI risk management and traceability, not the detection accuracy, cost, or legal status of a specific AI code review service. Therefore, set acceptance thresholds yourself within this limited trial and treat the outcome as local decision information, not as a general performance claim.
