Free PDF Toolkit • August 2026 Enforcement Edition
EU AI Act Article 10 Checklist (PDF)
An audit-ready Data Quality & Lineage Compliance Checklist for teams building or operating
high-risk AI systems under the EU AI Act.
Built by AI Governance Desk to help teams document controls, evidence, and traceability in a way that holds up under review.
What this checklist helps you prove
- Dataset scope & ownership: identify all training/validation/testing datasets and accountable owners.
- Origin & collection controls: document lawful sourcing, collection method risks, and known gaps.
- Preparation & annotation governance: versioned preprocessing, labeling consistency, and bias risks.
- Data quality & representativeness: evidence for relevance, completeness, error controls, and fairness testing.
- Lineage & traceability: end-to-end mapping from source → transformation → model version → deployment.
- Post-market monitoring: drift, bias re-testing, incident logging, and continuous compliance readiness.
Who it’s for
- Compliance & risk leaders responsible for EU AI Act readiness
- Data governance teams managing quality, lineage, and documentation
- ML / AI engineering teams maintaining datasets and pipelines
- Internal audit teams preparing evidence and inspection workflows
What’s included inside
- 7 control sections aligned to Article 10 data governance expectations
- Audit Evidence Log Template (status tracking + ownership)
- Final Enforcement Readiness Review Checklist
- Compliance Attestation page (internal sign-off)
- Appendix: Sample Data Lineage Diagram Template
Preview (optional)
Want to quickly scan it before downloading? Use the embedded preview below.
FAQ
Does this checklist replace legal advice?
No. It’s a practical governance toolkit to help you document controls and evidence. Use it alongside your legal and regulatory review process.
Can we use this for internal audits?
Yes — that’s one of its best uses. The evidence log and attestation page are designed to support internal review and readiness reporting.
Is it only for teams training models?
It’s strongest for systems using training techniques, but it also supports testing and documentation needs for teams managing high-risk deployments.