Define the system
Start by naming the AI feature, users, decision impact, data categories, vendors, and the team member responsible for maintaining the review record.
Documentation template
EU AI Act Documentation Template
Use this starter structure to create a practical AI system file. It is useful for internal reviews, customer security questionnaires, investor diligence, and early preparation for higher-risk workflows.
Disclaimer: This template is not legal advice and is not a complete conformity assessment package. High-risk systems may require additional technical documentation, quality management, testing, monitoring, and specialist review.
Copyable system file outline
| Section | What to include |
|---|---|
| 1. System identity | Product name, feature name, owner, version, release date, markets, and review date. |
| 2. Intended purpose | The task the AI is designed to perform, target users, affected people, and excluded uses. |
| 3. Role and supply chain | Your role as provider, deployer, importer, distributor, or downstream integrator, plus model and vendor dependencies. |
| 4. System description | Architecture, model family, data flow, inputs, outputs, integrations, prompts, retrieval sources, and user controls. |
| 5. Risk classification | Prohibited-practice screen, high-risk screen, Annex III review, transparency obligations, and the reason for the chosen classification. |
| 6. Data and evaluation | Data categories, data quality checks, evaluation methods, representative test cases, known gaps, and bias review where relevant. |
| 7. Risk controls | Mitigations, guardrails, access controls, red-team findings, misuse cases, and residual risks. |
| 8. Human oversight | Who reviews outputs, when escalation is required, how overrides work, and what users are told. |
| 9. Logging and monitoring | Events logged, retention approach, issue review cadence, incident response, model-change review, and customer feedback channel. |
| 10. Instructions and notices | User instructions, limitations, AI transparency notice, support contacts, and customer admin guidance. |
| 11. Change history | Model changes, prompt changes, data-source changes, new markets, new customer use cases, and reviewer sign-off. |
Minimum memo for lower-risk features
- One paragraph describing the feature and intended purpose.
- A classification table covering prohibited, high-risk, limited transparency, and minimal-risk signals.
- A copy of the user-facing AI notice, if one is used.
- A dated owner sign-off and next review date.
High-risk evidence add-ons
- Risk management plan and testing evidence.
- Technical logs and record-keeping approach.
- Human oversight design and reviewer instructions.
- Accuracy, robustness, and cybersecurity notes.
- Post-market monitoring and incident workflow.
Official sources
Last reviewed: July 3, 2026.
- EU AI Act Article 11: technical documentation
- EU AI Act Annex IV: technical documentation contents
- EU AI Act Article 12: record-keeping
- EU AI Act Article 13: transparency and instructions for use
- EU AI Act Article 15: accuracy, robustness, and cybersecurity
practical AI compliance self-assessment
Practical notes for EU AI Act Documentation Template
EU AI Act Documentation Template | Practical AI System File is maintained for founders, product managers, compliance owners, agencies, and small teams building AI workflows who need AI governance workflow. The goal is to help visitors complete a real task and leave with an AI inventory, risk note, disclosure draft, vendor question set, policy outline, or review workflow report, not only read a generic summary.
Separate triage from advice
Use the generated output as first-pass operational triage. Legal, medical, hiring, credit, education, biometric, and public-sector uses still need specialist review.
Keep evidence
Save the output, assumptions, date, source links, and reviewer notes so the team can explain why a risk level, disclosure, or vendor question set was chosen.
Before relying on this page
- Review high-impact use cases manually.
- Keep policies and disclosures aligned with the real product behavior.
- Re-run the workflow when vendors, data, or user impact changes.
Review record
How to use EU AI Act Documentation Template in an AI compliance file
A compliance tool is useful when it leaves a traceable record. The output should name the AI system, explain the assumptions, and show what the team still needs to verify with product, legal, security, or vendor owners.
Describe the real system
Record the feature name, user group, decision impact, data sources, vendor dependencies, and the human owner. A generic "chatbot" label is rarely enough. A hiring assistant, support summarizer, medical triage bot, and product recommender can have very different risk profiles even if they all use language models.
Separate signal from conclusion
Treat this page as first-pass triage. It can flag high-risk areas, transparency duties, missing evidence, and questions to ask a vendor. It should not be treated as legal approval, clinical advice, hiring approval, credit approval, or permission to launch without human review.
Save evidence and changes
Save the generated result with the date, reviewer, source links, and unresolved questions. Update the record when the model, data, users, product flow, vendor, or region changes. This keeps the site useful for actual operators rather than only being a static explanation page.
Evidence checklist
- Document what the AI system does and what it does not do.
- Record whether the system influences employment, education, credit, public benefits, healthcare, biometric identification, safety, or other high-impact outcomes.
- Keep vendor documentation, model notes, data descriptions, user notices, human oversight notes, and monitoring plans together.
- Re-run the review when the product behavior changes, not only when the law changes.
- Use specialist review for high-impact or regulated workflows before relying on any generated text.