Foundations of AI & Cybersecurity - Lesson 33: Audit Model Output for Risks

This lesson explains that securing AI requires continuous auditing of what the model actually outputs, not just the infrastructure around it. It focuses on four major output risks: hallucinations, accuracy failures, bias, and unauthorizedaccess, and shows how each can lead to harmful decisions, compliance issues, or loss of trust. The central lesson is that enterprise AI becomes trustworthy only when its outputs are tested, reviewed, and governed on an ongoing basis.

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