Episode 3 — Walk through an AI system life cycle in clear, simple language (Task 22)
This episode builds a clean, exam-ready mental model of the AI system life cycle so you can consistently place risks, controls, and evidence in the right phase, which is central to Task 22 and frequently implied across other tasks. You’ll define each phase—from idea and intake through data collection, model development, training, evaluation, deployment, operations, change management, and retirement—and you’ll learn what “secure” means at each step in terms of access control, data integrity, safety validation, monitoring, and documented approvals. We’ll connect common failures to life-cycle blind spots, such as using production prompts in testing, retraining on untrusted data, or shipping a model change without updating impact assessments and runbooks. By the end, you’ll be able to hear a scenario and immediately say, “This is a lifecycle governance problem,” or “This is a pipeline control problem,” and pick the best next action. 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.
