As organizations increasingly rely on pre-trained models, open-source components, and third-party AI services, understanding what is actually inside a model has become a critical security, procurement, and compliance concern.

MLSBOM: Model Supply Chain Transparency explains how to create and maintain a Model Software Bill of Materials for AI and machine learning systems. Written for DevOps, security, and procurement teams, the book provides practical guidance on documenting model components in a way that supports risk assessment, vendor oversight, and lifecycle governance.

This volume extends traditional SBOM concepts into the AI domain, addressing the unique challenges posed by model weights, training data artifacts, embedded dependencies, and externally sourced components. It focuses on operational applicability rather than abstract standards discussion.

Key areas covered include:

• What constitutes a model bill of materials in practice


• Capturing model weights, architectures, and dependencies


• Documenting pre-trained artifacts and external sources


• Versioning, updates, and model change traceability


• Security, licensing, and usage risk implications


• Why procurement and security teams require MLSBOMs

Designed for organizations managing complex AI supply chains, this book provides clear structures and examples to improve transparency, reduce hidden risk, and align AI sourcing practices with modern security and governance expectations.