Securing the AI Pipeline – From Data to Deployment

In our first post, we established why securing AI workloads is mission-critical for the enterprise. Now, we turn to the AI pipeline—the end-to-end journey from raw data to deployed models—and explore why every stage must be fortified against evolving threats. As organizations accelerate AI adoption, this pipeline becomes a prime target for adversaries seeking to poison data, compromise models, or exploit deployment endpoints.

Enterprises don’t operate a single “AI system”; they run interconnected pipelines that transform data into decisions across a web of services, models, and applications. Protecting this chain demands a holistic security strategy anchored in Zero Trust for AI, supply chain integrity, and continuous monitoring. In this post, we map the pipeline, identify key attack vectors at each stage, and outline practical defenses using Microsoft’s security controls—spanning data governance with Purview, confidential training environments in Azure, and runtime threat detection with Defender for Cloud.

Our guidance aligns with leading frameworks, including the NIST AI Risk Management Framework and MITRE ATLAS, ensuring your AI security program meets recognized standards while enabling innovation at scale. A Security view of the ai pipeline Securing AI isn’t just about protecting a single model—it’s about safeguarding the entire pipeline that transforms raw data into actionable intelligence..