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

In AIBoMGen: Generating an AI Bill of Materials for Secure, Transparent, and Compliant Model Training, a controlled training platform observes datasets, configuration, environment, and artifacts as the job executes, then hashes the artifacts and produces signed AIBOM and in-toto evidence.

AIBoMGen makes that possible by restricting the training environment. Users provide inputs and parameters rather than arbitrary code or shell access, allowing the platform to remain an independent observer of a constrained workflow. This limits generality and leaves trust in the platform, workers, signing keys, containers, and cloud control plane, but demonstrates a broader principle: trustworthy provenance may require constraining execution enough that evidence generation cannot simply be bypassed.

For a related approach to authenticating ML transformation history with hardware-backed attestation and transparency logs, see my notes on Atlas.