Episode 48 — Run the AI risk management life cycle from intake to monitoring (Task 4)
This episode teaches the AI risk management life cycle as a repeatable workflow, which AAISM tests by asking what to do next when a new use case appears, when risks are discovered, or when monitoring shows unexpected behavior. You will learn how to run intake with clear scope, assumptions, and stakeholders, then perform risk identification and analysis across data, model behavior, deployment context, and user interaction. We explain how to choose treatments such as control implementation, design changes, process constraints, or risk acceptance, and how to document decisions so they hold up in audit and post-incident review. Troubleshooting focuses on breakdowns like intake that misses key dependencies, assessments that skip data integrity and logging, and monitoring that is not tied to thresholds or response actions. 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.
