Episode 97 — Test AI controls with evidence, not opinions or vendor demos (Domain 3B)
This episode teaches you how to test AI controls using evidence, because Domain 3B scenarios often tempt you to accept “trust me” statements, impressive demos, or subjective opinions as proof. You’ll learn how to define what evidence is required for common AI controls, such as approvals for model changes, validation reports tied to acceptance criteria, monitoring configurations with thresholds and escalation, access controls with logs, and supervision workflows with reviewer records. We’ll cover how to handle vendor-provided evidence by validating relevance, scope, timeliness, and responsibility splits, instead of assuming a generic report proves control effectiveness in your environment. You’ll also learn how to separate control design from operating effectiveness by looking for repeated performance over time, including trend reports, incident records, and follow-up actions that show governance responds to what monitoring reveals. By the end, you should be able to answer exam questions by selecting the option that produces verifiable evidence and traceable accountability, not the option that sounds most confident. 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.
