Episode 65 — Design AI security architecture with clear trust boundaries and data flows (Task 10)
This episode teaches how to design AI security architecture by clearly defining trust boundaries and data flows, because AAISM questions often hinge on whether you can place controls based on how information and authority actually move through the system. You will learn to map where data is collected, transformed, stored, and used for training or inference, and where identities, keys, and permissions enable actions across components. We walk through a scenario where an AI service connects to internal data sources and external vendor APIs, showing how trust boundaries identify where to enforce authentication, authorization, validation, and logging. Troubleshooting focuses on architecture diagrams that hide critical flows, boundary assumptions that are not true in production, and designs that cannot support investigation because telemetry and version history are not captured. 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.
