AIBoMGen: Generating an AI Bill of Materials for Secure, Transparent, and Compliant Model Training
Wiebe Vandendriesschea, Jordi Thijsmana, Laurens D’hoogea, Bruno Volckaerta, Merlijn Sebrechtsa
- aIDLab, Department of Information Technology, Ghent University - imec, Ghent, Belgium
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Abstract
The rapid adoption of complex AI systems has outpaced the development of tools to ensure their transparency, security, and regulatory compliance. In this paper, the AI Bill of Materials (AIBOM), an extension of the Software Bill of Materials (SBOM), is introduced as a standardized, verifiable record of trained AI models and their environments. A proof-of-concept platform, AIBoMGen, automates the generation of signed AIBOMs by capturing datasets, model metadata, and environment details during training. The training platform acts as a neutral, third-party observer and root of trust. It enforces verifiable AIBOM creation for every job. The system uses cryptographic hashing, digital signatures, and in-toto attestations to ensure integrity and protect against threats such as artifact tampering by dishonest model creators. Evaluations show that AIBoMGen reliably detects unauthorized modifications to all artifacts and can generate AIBOMs with negligible performance overhead. These results highlight the potential of AIBoMGen as a foundational step toward building secure and transparent AI ecosystems, enabling compliance with regulatory frameworks like the EU’s AI Act.
TL;DRThe AI Bill of Materials (AIBOM), an extension of the Software Bill of Materials (SBOM), is introduced as a standardized, verifiable record of trained AI models and their environments as a foundational step toward building secure and transparent AI ecosystems. (Semantic Scholar)