Episode 79 — Evaluate the design and effectiveness of AI-specific controls (Task 12)
This episode focuses on evaluating the design and effectiveness of AI-specific controls, because Task 12 is about proving that controls exist for AI risks that traditional IT controls do not fully address. You’ll learn how to identify AI-specific controls across data governance, model validation, explainability requirements, drift monitoring, human oversight triggers, and change management that treats model updates as outcome-changing events. We’ll cover how to evaluate control design by checking whether each control addresses a defined risk, whether it has an owner, whether it can be performed consistently, and whether it produces evidence that can be sampled and verified. You’ll also learn how to evaluate effectiveness by looking for operational results: fewer harmful outcomes, timely escalations, consistent documentation, and changes to controls when monitoring reveals weakness. By the end, you should be able to choose exam answers that emphasize well-designed, testable controls tied to risk and evidence, not generic statements like “follow best practices.” 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.
