Episode 23 — Classify AI assets by sensitivity, criticality, and compliance scope (Task 13)
This episode expands Task 13 by showing how to classify AI assets using sensitivity, criticality, and compliance scope, because AAISM questions frequently ask you to choose controls and governance actions that match the asset’s impact if it fails, leaks, or behaves unexpectedly. You’ll define classification dimensions that matter for AI systems, including data confidentiality in prompts and outputs, integrity requirements for decision support, availability needs for business operations, and regulatory obligations based on jurisdictions, user populations, or data categories. We’ll work through a scenario where the same model supports both internal drafting and regulated customer interactions, and you’ll practice classifying the model, its datasets, and its inference logs differently based on how they are used and who can access them. Best practices include aligning AI classifications to existing enterprise data classification schemes, documenting rationale so it is auditable, and using classification to drive access reviews, monitoring depth, retention rules, and incident prioritization. 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.
