AI Risk & Governance Checklist
1. Risk Identification & Classification
- [ ] Determine if the AI falls under unacceptable, high, limited, or minimal risk categories
- [ ] Check if it qualifies as general-purpose AI (GPAI) or an agentic system with autonomy
- [ ] Map jurisdictional scope (EU AI Act, GDPR, national laws, global markets)
2. Governance & Accountability
- [ ] Assign a clear accountable owner for AI compliance
- [ ] Establish an AI governance framework (policies, committees, escalation paths)
- [ ] Define roles for provider, deployer, distributor, importer as per EU AI Act
3. Data Management & Quality
- [ ] Ensure datasets are representative, relevant, and documented
- [ ] Conduct bias and fairness audits during data prep
- [ ] Apply data protection by design (minimization, anonymization, lawful basis)
4. Design & Development
- [ ] Perform risk assessments at each development stage
- [ ] Document model design, training, and limitations
- [ ] Implement security by design (adversarial robustness, penetration testing)
5. Transparency & Documentation
- [ ] Maintain technical documentation (model cards, data sheets, intended use)
- [ ] Provide instructions for use to downstream deployers
- [ ] Clearly state capabilities, limitations, and error rates to users
- [ ] Log training data sources, model changes, and decision flows
6. Human Oversight & Control
- [ ] Ensure human-in-the-loop (HITL) or human-on-the-loop (HOTL) mechanisms
- [ ] Provide means to override or shut down the system safely
- [ ] Train users in effective oversight and decision review
7. Testing & Validation
- [ ] Conduct pre-deployment testing for accuracy, robustness, safety
- [ ] Simulate adversarial and misuse scenarios
- [ ] Validate against compliance and ethical standards
8. Deployment & Monitoring
- [ ] Keep continuous monitoring for performance, drift, anomalies
- [ ] Log significant events for traceability and accountability
- [ ] Collect user feedback and incident reports systematically
- [ ] Establish a decommissioning process when systems are retired
9. Impact & Rights Assessment
- [ ] Conduct Fundamental Rights Impact Assessment (FRIA) if risk is non-trivial
- [ ] Map risks to privacy, equality, safety, freedom of expression, employment
- [ ] Document mitigation strategies for identified harms
10. Regulatory Compliance
- [ ] Verify obligations under EU AI Act (risk tier-based)
- [ ] Ensure compliance with GDPR, cybersecurity acts, consumer protection laws
- [ ] For high-risk systems, prepare conformity assessment files
- [ ] Track timelines for phased compliance obligations
11. Security & Cyber-resilience
- [ ] Secure model against data poisoning, adversarial inputs, model extraction
- [ ] Protect infrastructure from cyber-attacks
- [ ] Monitor for misuse and malicious repurposing of outputs
12. Culture & Training
- [ ] Provide responsible AI training to developers, managers, deployers
- [ ] Build a culture of responsibility, questioning, and escalation
- [ ] Encourage reporting of ethical or compliance concerns
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