AI Bill of Materials (AIBOM) — Inventory for AI Systems | CertifiedData.io

An AI Bill of Materials, or AIBOM, extends the logic of software supply chain visibility into AI systems. That means accounting not only for software dependencies, but also for datasets, models, evaluation assets, prompts, and governance records.

AIBOM matters because AI systems are shaped by more than code. They depend on artifacts whose provenance, identity, and integrity often need stronger treatment than ordinary software inventories provide.

This hub groups the core AIBOM pages that explain how component transparency, AI governance, and artifact verification fit together.

Why AIBOM is different from SBOM

A Software Bill of Materials (SBOM) catalogs software packages and their versions. An AI Bill of Materials must go further — it must document training datasets, model weights, fine-tuning procedures, evaluation benchmarks, and the provenance of every data artifact that shaped a model's behavior.

This distinction matters because AI system failures often originate in data: biased training sets, contaminated evaluation benchmarks, undocumented synthetic data sources. An AIBOM creates accountability at the data layer, not just the code layer.

Required AIBOM components

Training datasets

CertifiedData

The complete inventory of datasets used for pre-training and fine-tuning. Includes origin, volume, synthetic/real classification, and any applicable licenses.

Base model provenance

The specific model checkpoint, version, and source — including third-party models, APIs, and open-source weights used as components.

Evaluation benchmarks

CertifiedData

The datasets and metrics used to measure model performance. Certified benchmarks prevent post-hoc contamination claims.

Fine-tuning data

CertifiedData

Instruction datasets, RLHF preference data, domain-specific examples, and alignment data applied on top of a base model.

Data pipeline transforms

Preprocessing steps, filtering rules, deduplication procedures, and augmentation methods applied to raw training data.

Third-party components

External embeddings, APIs, retrieval stores, and model adapters that contribute to the system's outputs.

How to build an AIBOM

Inventory all data inputs

Catalog every dataset used at any stage: pre-training, fine-tuning, evaluation, and alignment. Record origin, volume, format, and synthetic/real classification.

Certify dataset components

For each dataset, generate a cryptographic certificate that proves its origin and integrity. CertifiedData issues Ed25519-signed certificates with SHA-256 dataset fingerprints.

Record model lineage

Document the base model, all fine-tuning stages, and the evaluation protocol. Link each stage to its certified data inputs.

Anchor to a registry

Publish certificate IDs to a public or private artifact registry. Auditors can independently verify any component without accessing the underlying data.

AIBOM JSON structure with CertifiedData anchors

{
  "aibom_version": "1.0",
  "system_name": "Risk Scoring Model v3",
  "components": [
    {
      "type": "training_dataset",
      "name": "Synthetic Credit Transactions",
      "rows": 500000,
      "synthetic": true,
      "certifieddata": {
        "certificate_id": "cert_01j9k...",
        "dataset_hash": "sha256:a3f9...",
        "algorithm": "CTGAN",
        "issuer": "Certified Data LLC",
        "verify_url": "https://certifieddata.io/verify/cert_01j9k..."
      }
    },
    {
      "type": "evaluation_benchmark",
      "name": "Fraud Detection Holdout",
      "rows": 50000,
      "certifieddata": {
        "certificate_id": "cert_02m4p...",
        "dataset_hash": "sha256:b7d2...",
        "issuer": "Certified Data LLC"
      }
    }
  ]
}

Regulatory drivers

The EU AI Act Article 10 requires high-risk AI systems to document training, validation, and testing datasets including their origin, characteristics, and any preprocessing. An AIBOM provides the structured evidence format that satisfies this obligation.

NIST AI RMF 1.0 calls for AI system transparency and documentation throughout the development lifecycle. AIBOM aligns directly with the GOVERN and MAP functions — particularly around data governance and risk documentation.

Enterprise procurement teams increasingly require supplier AIBOMs before integrating AI components. The AIBOM is rapidly becoming the AI-equivalent of SOC 2: not yet universally mandated, but increasingly expected.