Inadequate Monitoring and Logging

AI systems may behave unpredictably or degrade in subtle ways over time. Without sufficient monitoring and logging, these behaviors may go undetected, leaving organizations blind to risks such as adversarial inputs, performance degradation, hallucinations, or unauthorized access. Moreover, when incidents do occur, a lack of logs can prevent forensic analysis, accountability, or compliance with reporting requirements. Monitoring should include both technical telemetry and tools for human feedback, especially in high-impact or safety-critical settings.

Common Pattern: Silent model degradation in deployed ML models goes unnoticed due to a lack of output monitoring or feedback integration. It can cause problems such as performance issues or possible attacks if it goes unnoticed.