Model Provenance

Definition

Model provenance is the complete documented history of an AI model’s creation, training, modification, and distribution — analogous to chain-of-custody documentation in forensics or provenance records in art authentication. Provenance records include the training data sources, training methodology, fine-tuning history, evaluation results, cryptographic signatures, and distribution path. Verified provenance enables organizations to determine whether a model they are deploying is the authentic, unmodified artifact produced by the claimed provider.

How It Relates to AI Threats

Model provenance is a defensive mechanism within Security & Cyber that counters supply chain attacks. Without verified provenance, organizations cannot distinguish between a legitimate model and a compromised version containing embedded backdoors. Provenance verification through cryptographic signing (Sigstore, cosign) and hash verification enables detection of tampering at any point in the distribution chain. The C2PA (Coalition for Content Provenance and Authenticity) standard and AI-SBOM (AI Software Bill of Materials) concept extend provenance tracking to the full AI component inventory.

Why It Occurs

• The AI supply chain depends on components from public registries where provenance verification is optional, not mandatory
• Model weights are opaque binary artifacts that cannot be inspected through code review
• Organizations often lack infrastructure for cryptographic verification of model artifacts
• The concept of model provenance is newer than software provenance, and tooling is still maturing

Real-World Context

Hugging Face has introduced Sigstore-based model signing for its Hub, enabling cryptographic verification of model provenance. The SLSA (Supply chain Levels for Software Artifacts) framework is being adapted for ML pipelines, providing graduated assurance levels from basic provenance logging to hermetic builds with non-falsifiable provenance. The EU AI Act requires AI providers to maintain documentation of model development and supply chain — effectively mandating provenance tracking for high-risk AI systems.