AIBoMGen: Generating an AI Bill of Materials for Secure, Transparent, and Compliant Model Training

TL;DR

This paper presents AIBoMGen, a platform that automates the creation of secure, verifiable AI Bills of Materials to enhance transparency, security, and regulatory compliance in AI model training.

Contribution

It introduces AIBoMGen, a novel system that automatically generates cryptographically signed AI Bills of Materials during training, ensuring integrity and transparency.

Findings

AIBoMGen reliably detects unauthorized artifact modifications.

The system adds negligible performance overhead.

It supports compliance with AI regulations like the EU AI Act.

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. Our 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. Our evaluation demonstrates 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 EUs AI Act.