Episode 50 — Assign AI risk owners and approvals so accountability is never unclear (Task 4)
This episode completes the set by teaching Task 4’s accountability core: assigning AI risk owners and approvals so responsibility is explicit, decisions are traceable, and risk acceptance is intentional rather than accidental. You’ll define what it means to “own” AI risk, including being accountable for controls, monitoring outcomes, exception handling, and lifecycle changes that alter exposure, and you’ll learn how approval pathways should work for new use cases, high-impact deployments, vendor changes, and policy exceptions. We’ll use a scenario where multiple teams share one AI platform, creating blurred lines between platform operators, business owners, and data owners, and you’ll practice building an approval model that clarifies who can accept risk, who must be consulted, and what evidence must be produced before approval is valid. Troubleshooting covers common breakdowns like shared ownership with no decision authority, approvals that ignore data and vendor dependencies, and “temporary” exceptions that never get re-evaluated. 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.
