For Heads-of · Practitioner
AI Bill of Materials
Also known as: AI-BOM · AIBOM · ML-BOM · AI SBOM
A machine-readable inventory of what an AI system is built from: models, datasets, adapters, software dependencies, and their provenance and licences. It extends the software bill of materials to the components that make AI systems different.
- SBOM
- supply chain
- CycloneDX
- SPDX
- provenance
What it records
For each component: what it is, its version, where it came from, its licence and how it relates to the others. For models, typically the base model and any fine-tuning; for datasets, their sources and processing. Two standard formats cover it: CycloneDX, whose machine learning bill of materials (ML-BOM) is part of the core specification, and SPDX 3.0, which added AI and dataset profiles in 2024.
Why it matters now
In May 2026 CISA and its G7 partners, including the UK, published voluntary minimum elements for a software bill of materials for AI, which gives buyers a common checklist to ask for. An AI bill of materials answers the questions that matter in an incident or a vulnerability disclosure: which of our systems use this model, this dataset, this library?
Using it
Ask suppliers for one as part of third-party risk due diligence, produce one for systems you build, and link both from the AI inventory. The register entries it supports are third-party model and dataset provenance and LLM application supply chain vulnerabilities; training data provenance checks is the matching control.
Sources
Related Terms
AI Inventory
A maintained register of every AI system, model and AI-enabled feature an organisation uses, builds or buys, with an owner, a purpose, a risk tier and a status for each. It is the first control in every AI governance framework because none of the others can be applied to a system nobody has listed.
AI Supply Chain Security
Assuring the integrity and provenance of everything an AI system is built from and depends on: pre-trained models and weights, adapters, datasets, libraries, serving infrastructure, plug-ins and tools. AI adds components that ordinary software supply-chain controls were not built to check.
Model Card
A document describing a machine learning model's intended uses, performance, limitations and known risks, so that people deciding whether and how to use it can do so on evidence. System cards extend the idea to the deployed system and its safeguards, and EU AI Act documentation duties now require much of the same content.
Third-Party Risk
The exposure an organisation carries through dependencies on external parties who build, train, host or operate the AI systems it uses. In AI the exposure is characteristically deeper than the contract: beneath most vendors sits a model provider, and beneath that an infrastructure provider.