SBOM vs AIBOM vs MLBOM: What's the Difference?
TL;DR · the short version
A bill of materials (BOM) is a structured inventory of what a system is made of. An SBOM (Software Bill of Materials) lists an application's software components and dependencies — the established software supply-chain practice. Two AI extensions followed: the AIBOM (AI Bill of Materials), covering models, training data and surrounding software; and the MLBOM (Machine Learning Bill of Materials), focused on ML artifacts like models, datasets and provenance. They are complementary: an AI product typically needs an SBOM plus an AIBOM/MLBOM.
Do SBOMs capture training data provenance and model design, making an AIBOM unnecessary?
No. NTIA describes an SBOM as a formal record containing the details and supply chain relationships of software components, which is a record of components. CycloneDX describes its ML-BOM as representing information about models, datasets and their dependencies, including the provenance of datasets, training methodologies and the configuration of AI frameworks. The SPDX 3.0.1 AI profile defines properties such as typeOfModel, informationAboutTraining, hyperparameter and limitation. A record of components alone does not carry those AI-specific fields.
Which tools can include AI models and datasets in a bill of materials?
Documented capabilities vary. The OWASP AIBOM Generator builds a CycloneDX AIBOM from AI models hosted on Hugging Face. cdxgen lists AI-BOM support for prompt files, AI services, MCP configs and model metadata. Snyk's snyk aibom command outputs CycloneDX v1.6 JSON and detects models, agents, tools and MCP components in Python, Java, JavaScript and Go projects; its documentation does not clearly confirm dataset detection. The CycloneDX ML-BOM format itself covers datasets, so check whether a specific tool populates them.
Sources checked on 3 November 2026: NTIA: minimum elements for an SBOM CycloneDX ML-BOM SPDX 3.0.1 AI profile OWASP AIBOM Generator cdxgen Snyk docs: snyk aibom.
"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?"
Useful next, if this was relevant to you
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. AIBOM standards and templates compared sets the formats side by side, including Croissant for datasets.
What an MLBOM looks like in practice
An MLBOM is usually not a separate file format. It is a CycloneDX or SPDX document that describes machine-learning components alongside, or instead of, ordinary software packages.
- CycloneDX ML-BOM: CycloneDX (an OWASP standard) added machine-learning support in version 1.5. It includes a
machine-learning-modelcomponent type and model-card fields for intended use, limitations and performance metrics. Datasets can be described as data components and linked to the model. CycloneDX ML-BOM: fields and a worked example walks through a complete file. - SPDX 3.0 AI and Dataset profiles: SPDX 3.0 introduced profiles that describe AI packages and datasets in the same document as the software SBOM. See what the SPDX AI and Dataset profiles record, with an example.
A useful MLBOM entry typically records:
- model name, version and supplier
- licence
- the base model it was derived from (lineage)
- training and evaluation datasets with their sources
- cryptographic hashes of the weights, so you can verify integrity
- model-card details: intended use and known limitations
The three bills of materials compare as follows:
- SBOM: covers libraries and packages. The question it answers: what code is in this?
- MLBOM: covers models and datasets, with their provenance. The question it answers: which model, trained on what data?
- AIBOM: covers the whole AI system, from models and data to the software around them. The question it answers: what is this AI product made of, end to end?
Getting started
Generate and inspect an AIBOM for an AI system with the AIBOM generator →, compare the concepts side by side with AIBOM vs SBOM, and convert ML artifacts with the ML-BOM converter.
Frequently asked questions
What goes into an SBOM? An SBOM lists each software component and dependency with its name, version, supplier and licence, plus the relationships between them. Standard formats such as CycloneDX and SPDX define exactly which fields to capture.
What is a CBOM, and how does it relate to an SBOM? A CBOM (Cryptography Bill of Materials) inventories the cryptographic assets — algorithms, keys and certificates — in a system, which matters for post-quantum migration. It belongs to the same extended 'xBOM' family as the SBOM, AIBOM and MLBOM, each inventorying a different layer.
What is an AI SBOM? 'AI SBOM' is another name for an AIBOM — an SBOM-style inventory extended to an AI system's models, training data and surrounding software. It exists because a traditional SBOM does not capture model weights or datasets.
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.
Frequently asked questions
What is an MLBOM?
An MLBOM (Machine Learning Bill of Materials) is an inventory of the machine-learning artifacts in a system: models, datasets, and their provenance and lineage. In practice it is written in an existing BOM format, either CycloneDX (version 1.5 onward supports machine-learning models) or SPDX 3.0 (AI and Dataset profiles). That way it can sit alongside the software SBOM rather than in a separate toolchain.
What is the difference between SBOM, AIBOM and MLBOM?
An SBOM (Software Bill of Materials) inventories the software components, libraries and dependencies in an application. An AIBOM (AI Bill of Materials) extends that idea to AI systems, inventorying models, training and fine-tuning data, and the surrounding software — the full makeup of an AI product. An MLBOM (Machine Learning Bill of Materials) focuses specifically on machine-learning artifacts: models, datasets, and their provenance and lineage. AIBOM is the broadest for AI systems; MLBOM zooms in on the ML artifacts; SBOM covers the conventional software.
Why did AIBOM and MLBOM emerge if SBOM already existed?
Because a standard SBOM was designed for software components and does not capture the things that carry the most risk in AI systems: which model is used and where it came from, what data it was trained or fine-tuned on, licensing of models and datasets, and model provenance and integrity. AIBOM and MLBOM extend the bill-of-materials concept to make those AI-specific components visible and governable.
What formats are used?
The established SBOM formats — CycloneDX and SPDX — are extending to cover AI and ML components, so AIBOM and MLBOM are increasingly expressed as extensions of those same formats rather than entirely new standards. That means the tooling and processes you already use for SBOMs can often be extended to AIBOM/MLBOM rather than replaced.