Foundations of AI & Cybersecurity - Lesson 13: Secure Model Engineering and Risk Controls

This chapter explains why AI security must be engineered into the model from the beginning, not added after deployment. It focuses on three foundational risks during model creation: poisoning, manipulation, and drift, and shows how weak development, evaluation, or validation can embed long-term vulnerabilities. If these risks are not addressed early, the model may carry hidden weaknesses into every later stage of use.

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