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2026-07-06 · AIcap Guides

AI-BOM vs SBOM: why your SBOM misses your models

If you already generate an SBOM, you might assume your AI supply chain is covered. It isn't. A conventional SBOM answers "what packages did we install?" — an AI-BOM answers "what learns, infers, or decides in this system, and where did it come from?" Those are different questions with different failure modes.

What a standard SBOM sees — and what slips through

Run a typical SBOM generator against an ML service and you get the Python and OS packages: torch 2.4.0, transformers 4.44.2, openssl, and so on. Useful. But the highest-risk artefacts in an AI system are not packages:

ArtefactIn a standard SBOM?Why it matters
Model weight files (.safetensors, .pt, .gguf, .onnx)No — not installed via a package managerUnknown provenance, unknown licence, unscanned for tampering (OWASP ML05/ML06)
Weights baked into container layersNo — invisible without layer inspectionThe image ships to prod carrying a model nobody inventoried
Hosted model API usage (openai, anthropic SDK calls, hardcoded model IDs)Only as an SDK package nameThe actual dependency is a third-party model that changes under you
Model licences (OpenRAIL, Llama licence, research-only)NoAI model licences carry use restrictions package licences don't
Datasets and training-data references (DVC files, HF dataset imports)NoData provenance is an EU AI Act Annex IV requirement
GPU infrastructure (Terraform/K8s GPU requests)NoCost and capacity are part of the system description regulators expect

Each row is something the EU AI Act's Annex IV Section 2 or a serious security review will ask about, and each is invisible to a package-manager walk.

What an AI-BOM adds

An AI-BOM scanner starts from the same manifests an SBOM tool reads (requirements, lockfiles, package.json, go.mod) but adds AI-specific detection:

Not either/or: the two compose

This is not an argument to replace your SBOM. The right shape is an AI-BOM that *exports to* the SBOM standard your security tooling already ingests. CycloneDX 1.5 has model-card and ML extensions, so AI components can ride the same rails as everything else — Dependency-Track, GUAC, procurement portals. (We cover the export workflow in the CycloneDX guide.)

Who is asking for AI-BOMs

The operational rule

Inventories rot at the speed of your merge rate, so the only AI-BOM worth having is the one generated by the pipeline. One CI job, running on every push, emitting the inventory plus the risk register — that is the whole setup. The AIcap CLI does exactly this (free, MIT-licensed, and your source never leaves the runner); the GitHub Actions tutorial in this series has the copy-paste workflow.

AIcap generates your AI-BOM, risk register, and Annex IV draft from a single CI run — free CLI, EU-hosted ledger.

Get started free →