Episode 49 — Connect AI risks to enterprise risk reporting and decision-making (Task 4)
This episode focuses on Task 4 by showing how to connect AI risks to enterprise risk reporting and decision-making, because AAISM expects AI risk to be expressed in the same language leaders already use for prioritization, funding, and acceptance decisions. You’ll learn how to translate AI-specific concerns—like prompt injection, model drift, unsafe automation, and vendor dependency—into risk statements that include asset scope, threat or failure mode, business impact, likelihood drivers, current controls, residual risk, and clear ownership. We’ll walk through a scenario where a regulated business unit wants to use AI for customer interactions, and you’ll practice framing the risk so leadership can decide whether to proceed, what controls must be added, and what residual risk is acceptable. Best practices include consistent severity scales, mapping to enterprise risk categories, and documenting acceptance decisions with evidence so later audits or incidents don’t reveal hidden assumptions. 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.
