AI Artifact Certification

An AI artifact is any structured output produced by or used within an AI system — a synthetic dataset, a training corpus, a model checkpoint, an evaluation report, or a decision log bundle. As AI systems proliferate across regulated industries, the question of artifact provenance has become a governance requirement rather than an engineering preference. Which dataset trained this model? Was it modified after the audit? Who issued the certificate, and can the signature be independently verified?

CertifiedData operates as a certificate authority for AI artifacts. At certification time, the platform computes a SHA-256 fingerprint of the artifact, records structured metadata — algorithm, row count, column count, generation timestamp, issuer — and signs the entire payload with an Ed25519 private key. The resulting certificate is a machine-readable JSON record, not a badge or PDF. It can be independently verified by any party with access to the public signing key published at the CertifiedData well-known registry endpoint.

Certification does not modify the artifact. It produces a separate, tamper-evident record that proves: this exact artifact existed, with this exact content, at this exact time, issued by this exact authority. That is the same trust model TLS certificate authorities use for websites — applied to AI artifacts.

The term AI artifact covers any structured object that plays a role in the AI system lifecycle. In practice, this includes: synthetic datasets used for model training or testing; training datasets assembled from real or synthetic sources; model weights and checkpoints; evaluation reports produced by benchmarking pipelines; AI-generated output bundles such as classification results, forecasts, or recommendation sets; and governance artifacts such as decision log bundles and audit manifests.

CertifiedData currently focuses on synthetic dataset certification as the primary artifact type. The underlying registry architecture supports multiple artifact types, enabling future certification of model artifacts and governance bundles without schema changes. Each artifact record carries an artifact_type field — synthetic_dataset, training_dataset, model_artifact, evaluation_report, ai_output_bundle — allowing the registry to grow alongside platform capabilities.

AI systems are trained and evaluated on data. If the data changes between audit and production, the audit is meaningless. If a model was trained on a dataset that was later modified, any compliance claim based on that training run becomes unreliable. Without a tamper-evident record tying an artifact to a specific version at a specific time, AI governance is opinion rather than proof.

Cryptographic certification solves this by producing a verifiable chain: the artifact fingerprint is computed at generation time, embedded in the certificate payload, and signed by the issuer private key. Verification requires only the artifact, the certificate, and the public key — no dependency on the issuing platform's availability. This is the same architecture used by software package signing, code signing certificates, and TLS certificate authorities.

EU AI Act Articles 12 and 19 require high-risk AI systems to maintain records sufficient to enable post-hoc reconstruction of system behavior. Certified artifact records satisfy this requirement by providing machine-readable, independently verifiable proof of dataset provenance and integrity.

At certification time, CertifiedData performs four operations. First, the artifact content is read and hashed using SHA-256. For JSON artifacts, the hash is computed over a canonicalized representation using RFC 8785 JSON Canonicalization Scheme to eliminate serialization variance. For CSV and Parquet artifacts, the hash is computed over the raw file bytes. Second, a certificate payload is assembled: certification_id, timestamp, issuer, dataset_hash, algorithm_spec, dataset_metadata, schema_version.

Third, the payload is serialized to a canonical UTF-8 string and signed using the Ed25519 issuer private key (CERT_SIGNING_PRIVATE_KEY_PEM). The signature is base64url-encoded and appended to the certificate record. Fourth, the certificate is persisted to the CertifiedData registry and optionally emitted to the DecisionLedger public log for lineage traceability. The full certificate is returned to the requester as a structured JSON record.

Verification is the inverse: hash the artifact, compare against certificate dataset_hash, then verify the Ed25519 signature using the public key from the well-known endpoint. If both checks pass, the artifact is confirmed to be exactly the artifact the certificate was issued for.

Every certified artifact is registered in the CertifiedData artifact registry. The registry record links the artifact identity to its certificate, its generation metadata, and — where applicable — its lineage relationships. A dataset derived from another dataset carries a derived_from relationship. A dataset used to train a model can carry a used_to_train relationship pointing to the model artifact record. This graph structure enables full lineage reconstruction without requiring all artifacts to be certified on the same platform.

The artifact registry exposes public discovery at /dataset-marketplace and canonical artifact pages at /artifacts/:slug. Each artifact page links directly to its verification endpoint, creating the trust triangle: artifact identity, cryptographic proof, and public registry entry. This structure is indexed by search engines and auditable by compliance teams.

Certified artifacts support a lifecycle beyond simple issuance. An artifact can be in one of several states: certified (active, valid), superseded (replaced by a newer version), revoked (certificate invalidated by the issuer), or archived (retained for historical reference but no longer actively used). Supersession is non-destructive — the prior version remains publicly visible and verifiable, with a link to the superseding artifact. Revocation records the revocation timestamp and reason, and the verification endpoint reflects the revoked status in its response.

This lifecycle model is important for regulated environments where historical artifact records must be preserved. Revoked artifacts are not deleted — their certificates remain in the registry with a revoked status, and the artifact page shows the revocation event. This allows auditors to reconstruct what was certified, when, and what changed.

AI artifact certification is directly relevant to several regulatory frameworks. EU AI Act Articles 12 and 19 require technical documentation and logging sufficient to enable post-hoc audit of high-risk AI system behavior. Certified artifact records satisfy this by providing a machine-readable, independently verifiable evidence trail. SR 11-7 model risk management guidance requires documentation of model development data and methodology — certified training dataset records directly address this requirement.

The NIST AI Risk Management Framework (AI RMF) emphasizes traceability and documentation as core governance practices. Certified artifact provenance records integrate naturally into AI RMF-aligned governance programs. For organizations pursuing ISO/IEC 42001 AI management system certification, artifact certification provides the technical documentation layer required by the standard.