Episode 22 — Inventory AI assets: models, prompts, data, and key dependencies (Task 13)
This episode teaches how to build an AI asset inventory that is useful for security, audit, and incident response, which AAISM scenarios often test by asking what must be known before you can manage risk. You will define AI assets broadly to include models, training and evaluation datasets, prompt libraries, system prompts, embeddings, inference logs, endpoints, service accounts, secrets, and third-party dependencies such as APIs and managed platforms. We explain why “you can’t secure what you can’t see” becomes more complex in AI due to pipelines, rapid iteration, and shadow usage, then show how to capture ownership, environment, and data flow context so the inventory supports real decisions. Troubleshooting includes missing assets that hide risk, unclear owners that slow response, and inventories that are static documents instead of maintained control artifacts. 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.
