Why MLBOMs Are Useful for Securing the AI/ML Supply Chain

This article explains the need for Machine Learning Bills of Materials (MLBOMs) as a complement to traditional SBOMs, describing how MLBOMs catalog model components, training metadata, data provenance, ownership, and dependencies. It argues MLBOMs improve transparency, auditability, and governance for ML assets, and recommends integrating them into CI/CD pipelines, aligning them with business processes and policies, and using ML gates to manage risks from model drift, poisoned data, licensing, and operational changes.