Episode 31 — Monitor AI metrics to spot misuse, drift, and early incident signals (Task 18)
This episode explains how to monitor AI metrics in a way that reveals misuse, drift, and early incident signals before they become customer-impacting failures, which is a recurring AAISM exam expectation for operational readiness. You will learn to differentiate performance drift from security-relevant anomalies, then connect each metric to a practical response action, such as triggering deeper review, restricting access, or pausing a risky workflow. We walk through examples like sudden prompt patterns that indicate data exfiltration attempts, abnormal error rates that suggest endpoint abuse, and behavior shifts that may signal data poisoning or pipeline changes. Troubleshooting focuses on alert fatigue, weak thresholds, and missing ownership, because metrics only help when someone is accountable for interpretation and escalation. Produced by BareMetalCyber.com, where you’ll find more cyber audio courses, books, and information to strengthen your educational path. Also, if you want to stay up to date with the latest news, visit DailyCyber.News for a newsletter you can use, and a daily podcast you can commute with.
