Create comprehensive documentation for AI systems including technical specifications, compliance records, audit trails, and model inventories to satisfy regulatory requirements and enable effective governance.
AI documentation serves multiple critical purposes: regulatory compliance, operational continuity, audit readiness, and knowledge transfer. Under the EU AI Act, high-risk AI systems require extensive technical documentation that must be maintained throughout the system's lifecycle.
| Category | Purpose | Key Stakeholders |
|---|---|---|
| Technical Documentation | System design, architecture, specifications | Engineers, Auditors, Regulators |
| Compliance Records | Regulatory requirement mapping, evidence | Legal, Compliance, Regulators |
| Audit Trails | System activity logs, decision records | Auditors, Investigators |
| Model Inventories | Catalog of AI systems and their status | Governance, Risk Management |
| User Documentation | Instructions for safe and effective use | Deployers, End Users |
High-risk AI systems must have technical documentation drawn up before the system is placed on the market or put into service. Documentation must demonstrate compliance with requirements and provide authorities with necessary information for assessment.
Technical documentation provides a comprehensive description of the AI system enabling understanding of its design, development, and operation.
Model cards are standardized documents that communicate essential information about ML models in an accessible format.
Compliance records document how the organization meets regulatory requirements and provide evidence for audits and regulatory inquiries.
| Document | Content | Retention Period |
|---|---|---|
| Conformity Assessment | Evidence of EU AI Act compliance | 10 years after last unit marketed |
| DPIA/FRIA | Data protection and rights impact assessments | Duration of processing + 3 years |
| Risk Assessments | AI risk identification and mitigation | System lifecycle + 5 years |
| Bias Audits | Fairness testing results | Per jurisdiction (e.g., NYC: 4 years) |
| Training Records | Staff AI training and competency | Employment + 7 years |
| Vendor Assessments | Third-party AI due diligence | Contract term + 5 years |
Different jurisdictions have varying retention requirements. Organizations operating across multiple jurisdictions should retain documents for the longest applicable period. Document destruction must follow approved procedures and be logged.
Audit trails provide a chronological record of AI system activities, enabling investigation, accountability, and regulatory compliance.
| Event Category | Specific Events | Required Data |
|---|---|---|
| Model Lifecycle | Training, deployment, updates, retirement | Timestamp, actor, version, approval |
| Predictions/Decisions | Model inputs, outputs, confidence scores | Request ID, input hash, output, time |
| Human Overrides | Cases where humans override AI decisions | User, original decision, new decision, reason |
| Access Events | System access, data access, admin actions | User, action, resource, timestamp |
| Configuration Changes | Threshold changes, feature toggles, settings | Previous value, new value, approver |
| Incidents | Errors, anomalies, security events | Type, severity, response, resolution |
A model inventory is a centralized catalog of all AI systems within an organization, essential for governance, risk management, and regulatory compliance.
| Field | Description | Required |
|---|---|---|
| System ID | Unique identifier for the AI system | Yes |
| Name & Description | Human-readable name and purpose | Yes |
| Risk Classification | EU AI Act or internal risk level | Yes |
| Owner | Accountable individual or team | Yes |
| Lifecycle Stage | Development, Staging, Production, Retired | Yes |
| Data Sources | Training and inference data origins | Yes |
| Third-Party Components | External models, APIs, libraries | Yes |
| Compliance Status | Assessment results, certifications | Yes |
| Review Schedule | Next review date and frequency | Yes |