Securing the AI Pipeline – From Data to Deployment

The document emphasizes securing the AI pipeline, from data collection to deployment, as crucial for enterprise AI security. It highlights that the entire AI pipeline is vulnerable to threats like data poisoning and model tampering. A Zero Trust approach, involving continuous monitoring and supply chain integrity, is recommended. The document outlines key stages: data collection, preparation, model training, validation, registry, and deployment, each with specific risks. Microsoft's security controls, including Purview, confidential training environments, and Defender for Cloud, are suggested for mitigation. The approach aligns with NIST and MITRE AI security frameworks, advocating for a security-first blueprint. The provided reference architecture details the interplay of these security controls. Detailed examples of threats and mitigation strategies are provided for data collection/ingestion, data preparation, model training, and validation/testing.