Episode 57 — Design AI security testing that matches your model, data, and use case (Task 7)
This episode teaches how to design AI security testing that is fit for purpose, because AAISM questions often challenge you to choose testing that matches the model type, data flows, deployment context, and expected misuse patterns. You will learn to define test objectives such as resisting prompt injection, preventing data leakage, validating access boundaries, confirming logging coverage, and verifying guardrails under realistic user behavior. We use scenarios like an internal assistant with sensitive data access versus a public-facing chatbot to show how test depth and focus should differ, and how to document results so they support approvals and future retesting. Troubleshooting focuses on testing that is too generic, too theoretical, or detached from production controls, which creates false confidence and weak evidence when incidents occur. 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.
