Mini book: Securing the AI Stack: From Model to Production

The shift from ML experimentation to production forces us to confront a hard truth: our data pipelines and ML infrastructure aren't designed with adversarial thinking. I've seen too many teams treat security as a post-deployment concern, bolting on defenses after models hit production. The trifecta mentioned—AI-driven phishing, model poisoning, and cloud governance—hits directly at our architectural choices. Model poisoning especially demands we rethink data lineage and validation layers early. This means implementing immutable audit trails in your feature stores, treating training data provenance like you'd treat production schema changes, and adopting role-based access controls that extend beyond infrastructure into datasets themselves. The cloud governance angle matters because our infrastructure-as-code practices often leak blast radius across environments. We need to architect data platforms with assumption of compromise built in: isolated execution contexts for training, signed model artifacts, and strict separation between development and production data flows. Practically, this means investing in data governance frameworks now rather than after an incident forces your hand.