AI Model Provenance & SBOM for ML Artifacts |…

AI Model Provenance & SBOM for ML Artifacts

Track lineage, dependencies, licenses, and security for models, data, and pipelines to ensure auditability and trust.

Provenance and SBOM give verifiable traceability of data, code, models, and runtime dependencies. They underpin compliance, security, reproducibility, and incident response.

Training runs: code commit, hyperparams, seeds, hardware.

Framework/library versions, licenses, vulnerabilities.

Model artifacts, weights, tokenizer/vocab, configs.

live demo · related simulation● LIVE

CI/CD generates SBOM (e.g., Syft/CycloneDX) and attaches to artifacts.

Signed artifacts (Sigstore/COSIGN); attestations for builds/inference.

Evidence vault for audits: evals, red-teaming, approvals.

Frequently asked questions

What are sign artifacts and SBOM, and why is verification at deployment important?

Sign artifacts and SBOM; enforce verification at deploy.

How should evaluation and red-team results be attached to requests, and what approval process should be followed?

Attach eval/red-team results; approvals with timestamps/owners. It's crucial to document the evaluations thoroughly and obtain approvals from relevant stakeholders, including clear ownership and a timestamped record of the decision.

What steps should be taken to automate reporting for auditors, specifically regarding data lineage?

Automate reports for auditors: lineage graph + SBOM + evidence. Automated reporting allows for comprehensive tracking of data flow, alongside supporting documentation like SBOMs and relevant audit evidence.

Why are mandatory emits required for missing data lineage, and what happens if there are gaps in the emitted data?

Missing data lineage: mandatory emits; fail builds on gaps. Mandatory emissions ensure complete traceability of data throughout the system, and any missing data will automatically trigger a build failure to prevent inconsistencies.

▶ Try it live

Everything above runs in your browser — open Hash Function Avalanche Visualizer and change the parameters while it is running. Nothing is installed, nothing is uploaded, the whole model lives in one tab.

▶ Open Hash Function Avalanche Visualizer simulation