Episode 76 — Review and tune AI security controls as models, data, and threats change (Task 12)
This episode teaches how to review and tune AI security controls over time, because AAISM questions often assume that controls must evolve as models, data sources, vendor features, and attacker methods change. You will learn to build a review routine that uses monitoring signals, incident lessons learned, and reassessment triggers to decide what to tune, what to retire, and what to strengthen. We use examples like tightening prompt filtering after new abuse patterns, updating access scope when a use case expands, and retesting guardrails after a model update to show how tuning protects both safety and business outcomes. Troubleshooting focuses on control drift, including thresholds that become meaningless, policies that no longer match reality, and controls that were never revalidated after pipeline or vendor changes. 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.
