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

2026-01-09

arXiv.org

AI Summary

This work addresses the critical lack of transparency, safety, and regulatory compliance in rapidly deployed AI systems, which often hinders traceability of training data, environments, and associated metadata. To bridge this gap, the study introduces the concept of an AI Bill of Materials (AIBOM), extending the software bill of materials (SBOM) framework to the AI domain. The authors present AIBoMGen, a platform that automatically captures dataset, model, and environment metadata during training and leverages a neutral third party as a root of trust to generate cryptographically signed AIBOMs. By integrating cryptographic hashing, digital signatures, and in-toto attestations, the system ensures AIBOM integrity, non-repudiation, and tamper resistance, enabling efficient detection of unauthorized modifications with negligible performance overhead—thereby offering a foundational technical solution for compliance with emerging regulations such as the EU AI Act.

Technology Category

• Philosophy and Ethics of AI: Safety, Robustness & Trustworthiness
• Humans and AI: Explainable AI (XAI) for Human Understanding
• Intelligent Robots: Embodied AI

Application Category

• Security and Privacy: Data transparency and provenance
• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• Responsible Web: Algorithmic accountability and transparency on the web

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.

Problem

• AI transparency
• model security
• regulatory compliance
• AI Bill of Materials
• artifact integrity

Innovation

• AI Bill of Materials
• model transparency
• cryptographic attestation
• secure AI training
• regulatory compliance