Sample EU AI Act checkist

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…


This content originally appeared on DEV Community and was authored by Vishalendu Pandey

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


This content originally appeared on DEV Community and was authored by Vishalendu Pandey


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