Episode 42 — Build Continuous Monitoring, Maintenance, Updates, and Retraining Rhythms for Released AI
This episode focuses on what happens after launch, when an AI system must be monitored and maintained as a living system rather than treated as a finished product. You will learn why continuous monitoring matters for performance, fairness, security, drift, and user impact, and how maintenance, updates, and retraining should follow defined rhythms rather than ad hoc reactions. For the AIGP exam, the important point is that governance does not end at deployment. Released systems can degrade, face new threats, encounter changing data conditions, or produce new harms as their environment evolves. The episode also explores practical considerations such as threshold-based alerts, update approval processes, retraining triggers, change documentation, and rollback planning. In real organizations, disciplined post-release care reduces surprises because teams know what to watch, when to intervene, and how to preserve traceability as the system changes over time. 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. And dont forget Cyberauthor.me for the companion study guide and flash cards!
