# What Is an ML-BOM (Machine Learning Bill of Materials)?

> An ML-BOM inventories the machine-learning artifacts in a system — models, datasets and their provenance. What it captures, how it relates to SBOM and AIBOM, and why it matters.

Source: https://playciso.com/blog/ml-bom-explained · Published: 2026-09-26 · Publisher: PlayCISO (https://playciso.com)

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An ML-BOM — Machine Learning Bill of Materials — answers a narrow, high-value question: what machine-learning artifacts are in this system, where did they come from, and can we trust them? It is the model-and-data-focused cousin of the AIBOM, and it exists because the riskiest parts of an ML system are exactly the parts a traditional software inventory never sees. ## What an ML-BOM captures - Models: name, version, source/publisher, licence, and integrity (hash or signature). - Datasets: the data used to train and evaluate the models — source, licence, sensitivity. - Provenance and lineage: where each artifact came from and how it was produced or obtained. ## ML-BOM vs AIBOM vs SBOM An **SBOM** covers software components. An **AIBOM** covers a whole AI system, including the surrounding software plus models and data. An **ML-BOM** zooms in on the machine-learning artifacts specifically — often the model-and-data core of an AIBOM. They are complementary layers, not competitors, and a real AI product frequently needs more than one. ## Why it matters The model and its training data are where the most consequential, least-visible AI supply-chain risks live: a tampered or unknown-provenance model, a dataset with licensing or privacy issues, or a component you cannot verify. An ML-BOM makes those auditable — you can enforce licence policy, verify integrity, and know exactly what you are running. ## Formats and tooling ML-BOMs are increasingly expressed in **CycloneDX** or **SPDX**, the same formats as SBOMs, so you can extend existing tooling and pipelines rather than adopting a separate stack. Convert and inspect ML artifacts with the free [ML-BOM converter →](/tools/mlbom-converter), generate a full AI inventory with the [AIBOM generator](/tools/aibom), and compare the concepts in [SBOM vs AIBOM vs MLBOM](/blog/sbom-vs-aibom-vs-mlbom). ## Frequently asked questions **What is an ML-BOM?** A structured inventory of the machine-learning artifacts — models and datasets and their provenance — in a system. **How is it different from an AIBOM?** An AIBOM covers the whole AI system including software; an ML-BOM focuses on the ML artifacts, often the model-and-data core of an AIBOM. **Why do I need one?** The model and data are the riskiest, least-visible parts of an ML system — an ML-BOM makes them auditable. **What format?** CycloneDX or SPDX, extending existing SBOM tooling.