Model Provenance

Every artifact in the supply chain is assigned a unique, tamper-evident fingerprint using algorithms like SHA-256. This includes:

• Training Data: Hash of the entire dataset or its Merkle root.
• Source Code: Commit hash from the version control system.
• Model Weights: Hash of the final serialized model file.
• Environment: Hash of the container image or a lockfile specifying all dependencies. Any subsequent alteration to an artifact will produce a mismatched hash, immediately signaling a break in the provenance chain.

A tamper-proof, append-only ledger records every lifecycle event. This is not a simple text log but a structured, verifiable record. Key events include:

• Data Ingestion: Timestamp, source, schema, and checksum of the raw data.
• Training Initiation: User identity, hyperparameters, and the hash of the code and data used.
• Validation Completion: Benchmark results, evaluation metrics, and the hash of the resulting model.
• Deployment: Target environment, approver identity, and the model's unique version ID. This log provides a complete, non-repudiable audit trail for compliance and debugging.

Critical actions are cryptographically signed by authorized identities to establish non-repudiation. This proves who performed what action and prevents denial of involvement. The process uses a Public Key Infrastructure (PKI) :

• A data scientist signs an attestation that a dataset is approved for training.
• An ML engineer signs an attestation that a model passed all evaluation gates.
• A release manager signs an attestation authorizing production deployment. These signatures are stored in the immutable provenance log, creating a legally defensible chain of responsibility.

Provenance records enable automated, continuous verification of the entire supply chain. Before a model is deployed, a policy engine can cryptographically verify that:

• The training code has not been modified since its security review.
• The training data is the exact, approved dataset with no unauthorized additions.
• The model was trained in a certified, secure compute environment.
• All required human approvals were signed by authorized personnel. This policy-as-code approach prevents the deployment of models that deviate from a known, trusted state.

To establish a definitive point-in-time record, the root hash of the entire provenance tree is periodically published to a public, immutable ledger like a blockchain. This process, called anchoring, proves that the provenance record existed before a specific moment and has not been rewritten. Even if an internal system is compromised, the publicly anchored timestamp provides an independent, irrefutable proof of the model's lineage at that point in time, which is critical for intellectual property disputes.

Provenance records the external supply chain, while model fingerprinting provides an intrinsic, verifiable link to the final artifact. The final provenance record includes the model's unique fingerprint, extracted from its decision boundary. This creates a two-way binding:

• The provenance log points to a specific, unclonable model artifact.
• The model's fingerprint can be used to query the provenance log to retrieve its complete history. This coupling ensures that a model's documented lineage cannot be falsely attributed to a different, stolen copy.