# SBOM vs AIBOM vs MLBOM: What's the Difference?

> An SBOM inventories software components; an AIBOM covers AI systems including models and data; an MLBOM focuses on machine-learning artifacts. How the three bills of materials relate and when you need each.

Source: https://playciso.com/blog/sbom-vs-aibom-vs-mlbom · Published: 2026-09-26 · Publisher: PlayCISO (https://playciso.com)

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"SBOM, AIBOM, MLBOM — do I need all of them, and what is the difference?" is one of the fastest-growing questions in AI supply-chain security, and the confusion is understandable because the three overlap. The short version: they are layers of the same idea — a structured inventory of what a system is made of — applied to progressively more AI-specific components. ## The three, in one line each - SBOM (Software Bill of Materials): the software components, libraries and dependencies in an application. The established software-supply-chain practice. - AIBOM (AI Bill of Materials): the components of an AI system — models, training and fine-tuning data, and the software around them. The broadest AI-specific inventory. - MLBOM (Machine Learning Bill of Materials): the machine-learning artifacts specifically — models, datasets, and their provenance and lineage. ## Why the AI variants exist A standard SBOM was built to inventory software components. It does not naturally capture the parts of an AI system that carry the most supply-chain risk: which model you use and where it came from, what data it was trained or fine-tuned on, the licences attached to models and datasets, and model provenance and integrity. AIBOM and MLBOM extend the bill-of-materials concept to make those visible — so you can answer "what model and data is in this product, and can we trust their origin?" the way an SBOM lets you answer "what libraries are in this app?" ## Do you need all three? Frequently, more than one. A modern AI product is still software, so it needs an SBOM for its conventional dependencies, plus an AIBOM (or an MLBOM for the model-specific slice) to inventory models and data. What you emphasise depends on what you ship: a traditional application leans on SBOM; an AI application or a released model needs the AI-specific inventory layered on top. Encouragingly, the established formats — **CycloneDX** and **SPDX** — are extending to cover AI/ML components, so this is often an extension of your existing SBOM tooling rather than a separate stack. ## Getting started Generate and inspect an AIBOM for an AI system with the free [AIBOM generator →](/tools/aibom), compare the concepts side by side with [AIBOM vs SBOM](/tools/aibom-vs-sbom), and convert ML artifacts with the [ML-BOM converter](/tools/mlbom-converter). ## Frequently asked questions **What is the difference?** SBOM = software components; AIBOM = the whole AI system including models and data; MLBOM = the ML artifacts (models, datasets, provenance) specifically. **Do I need all three?** Often more than one — an AI product needs an SBOM for its software plus an AIBOM/MLBOM for its models and data. **Why did AIBOM/MLBOM emerge?** Standard SBOMs do not capture model provenance, training data or AI/ML licensing — the AI-specific risk. **What formats?** CycloneDX and SPDX are extending to AI/ML, so AIBOM/MLBOM often build on existing SBOM tooling.