Describe a CPMAI incident response workflow when an AI model misbehaves in production.

• A. Immediately remove all systems
• B. Detect anomaly, assess impact, contain, investigate root cause, remediate, verify, and communicate with stakeholders
• C. Silence the alarms and continue
• D. Rebuild the entire data lake from scratch

Answer: (B)

When a CPMAI system misbehaves in production, use a structured incident response workflow that moves from detection to communication. Start with detecting anomalies through monitoring and logs, which triggers the response. Then assess the impact to understand who and what is affected, the severity, and the business risk. Next, contain to limit harm, such as isolating the failing model, enabling a safe fallback, or rolling back to a known-good version. Investigate the root cause by examining data quality and drift, model performance, code changes, and infrastructure factors; gather evidence, reproduce the issue, and identify contributing factors. Remediate with targeted fixes like retraining the model, adjusting the data pipeline, gating or parameter-tuning features, or hardening the infrastructure. Verify the fix through testing, validation, and controlled production checks to ensure there are no regressions and that safety and performance targets are met. Finally, communicate with stakeholders to provide incident details, impact, resolution status, and lessons learned, while updating governance records for accountability.