AI Documentation
Producing clear, auditable documentation is central to AI compliance. Regulations like the EU AI Act, NIST AI RMF, and ISO/IEC 42001 all require organizations to generate detailed records that demonstrate risk management, oversight, and accountability.
For enterprises, robust documentation is not just a regulatory burden—it is also a market differentiator, proving trustworthiness to customers, investors, and regulators. This page outlines the major categories of AI compliance documentation.
Core Documentation Categories
| Document Type | Purpose | Examples |
|---|---|---|
| Risk Management Files | Identify, assess, and mitigate risks across the AI lifecycle | Risk register, hazard analysis, mitigation logs |
| Conformity Assessments | Demonstrate alignment with regulatory requirements | EU AI Act conformity assessment, ISO/IEC 42001 certification pack |
| Model Cards & System Cards | Provide transparency into model design, training, and limitations | Datasheets for Datasets, Model Cards for Model Reporting |
| Audit Logs | Enable traceability of model decisions and updates | Training data logs, inference request logs, change management history |
| Human Oversight Records | Document how humans monitor and intervene in AI systems | Oversight protocols, escalation procedures, override logs |
Sector-Specific Documentation
Different industries have additional obligations. For example, healthcare AI requires clinical evidence files, while finance requires algorithmic impact assessments.
| Sector | Required Documentation | Drivers |
|---|---|---|
| Healthcare | Clinical validation reports, safety monitoring logs | FDA, MDR (EU), HIPAA |
| Finance | Algorithmic impact assessments, audit trails of decisions | SEC, Basel III, AI in credit scoring rules |
| Mobility & Transport | Safety case documentation, conformity reports | UNECE, EU AI Act, national transport safety agencies |
| Employment & HR | Bias audits, explainability reports | EEOC (US), NYC Local Law 144 |
Documentation Lifecycle
AI compliance documentation is not a one-time deliverable. It must evolve with the system, covering the full AI lifecycle:
- Design Phase – risk analysis, ethical impact statements, data sourcing documentation
- Development Phase – dataset sheets, model training logs, validation protocols
- Deployment Phase – conformity reports, monitoring dashboards, transparency statements
- Post-Market Phase – ongoing surveillance reports, incident logs, periodic audits
Example Compliance Actions by Doc Type
| Document Type | Compliance Action | Enforcement Context |
|---|---|---|
| Model Card | Publish limitations, intended use, and training data characteristics | Transparency & trust obligations |
| Risk Register | Maintain up-to-date risk entries with mitigations | EU AI Act high-risk systems |
| Audit Logs | Track inference requests and system changes | Incident response and regulator inquiries |
| Oversight Protocol | Define human intervention and escalation paths | High-risk deployments like robotaxis or humanoid robots |