Episode 49 — Connect AI risks to enterprise risk reporting and decision-making (Task 4)
This episode explains how to connect AI risks to enterprise risk reporting so leadership can compare them against other priorities and make clear decisions, which AAISM frequently tests through reporting, escalation, and governance scenarios. You will learn to express AI risk in business terms by describing harm, likelihood, impact, affected stakeholders, and control effectiveness, then mapping those elements into the organization’s existing risk taxonomy and reporting cadence. We use examples like regulatory exposure from unsafe outputs, reputational harm from biased decisions, and operational risk from vendor dependency to show how AI risks become meaningful when framed consistently. Troubleshooting focuses on reporting failures such as overly technical language, missing risk owners, unclear residual risk statements, and dashboards that do not lead to decisions about funding, timelines, or acceptance. 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.
