Episode 57 — Design AI security testing that matches your model, data, and use case (Task 7)
This episode introduces Task 7 by teaching how to design AI security testing that matches your model, data, and use case, because AAISM expects you to test what can realistically fail in your specific deployment instead of applying generic security tests that miss AI-specific failure modes. You’ll define what “AI security testing” means here: validating access controls and data protections, probing for prompt injection and unsafe tool use, testing output safety and reliability boundaries, and confirming monitoring and logging are sufficient for investigation and audit. We’ll work through scenarios like a retrieval-augmented assistant with privileged data access and a customer chatbot with public inputs, showing how test design changes based on exposure, threat surface, and impact. Best practices include documenting test scope and results, linking findings to risk treatment decisions, and ensuring tests re-run after model updates, data changes, or configuration adjustments that can silently change behavior. 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.
