Episode 62 — Audit AI monitoring controls: drift, performance, and incident triggers (Task 8)
This episode teaches you how to audit AI monitoring controls for drift, performance, and incident triggers, because Task 8 expects monitoring to be designed and proven, not improvised after problems surface. You’ll learn how to define what must be monitored based on decision impact, including performance trends, stability of input data, fairness and segment outcomes where relevant, and operational signals like exception volume and manual overrides. We’ll cover incident triggers as explicit rules that convert monitoring into action, such as thresholds that require human review, escalation to governance forums, rollback, or retraining under controlled change management. You’ll also learn what evidence auditors should request, including metric definitions, data sources, alert rules, escalation runbooks, and records showing that triggers led to timely decisions and corrective action. By the end, you should be able to answer exam items by selecting the control approach that makes monitoring auditable, actionable, and aligned to risk appetite. 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.
