End-to-End Secure Machine Learning Development - Building a Secure AI Supply Chain

This tutorial examines security risks across the ML development lifecycle, from dataset collection and software dependencies to production deployment. It covers supply chain protections, model provenance, tracing poisoned training inputs, and limiting retraining to models affected by a framework compromise.