Corporate AI Compliance Checklist
1. AI Safety
- Risk assessment conducted for all AI systems (safety, robustness, misuse).
- Bias and fairness testing performed (e.g., using datasets like StereoSet).
- AI outputs validated for factual accuracy and reliability.
- Human-in-the-loop safeguards in high-risk applications.
- Incident response plan in place for AI failures.
- Data quality, integrity, and privacy controls applied.
- Alignment with: NIST AI RMF, OECD AI Principles, ISO/IEC 23894.
2. AI Governance
- AI Ethics Policy documented and communicated.
- AI oversight committee or accountable executive assigned.
- Lifecycle governance (design → training → deployment → monitoring).
- Third-party AI tools and vendors vetted for compliance.
- Logging and audit trails maintained for AI decisions.
- Regular employee training on AI ethics and compliance.
- Governance framework aligned with:
3. Privacy & Data Protection Compliance
- GDPR compliance (EU) → GDPR Regulation
- CCPA/CPRA compliance (California, US) → CPRA Official
- Singapore PDPA compliance → PDPA Guide
- China PIPL compliance → PIPL Translation
4. AI-Specific Regulations & Standards
- EU AI Act (risk-based compliance, 2025) → AI Act
- US AI Bill of Rights (principles for AI use) → AI Bill of Rights
- NIST AI RMF adoption (risk & trustworthiness) → NIST AI RMF
- OECD AI Principles (international) → OECD AI Principles
- ISO/IEC 42001 (AI Management System) → ISO/IEC 42001
5. Sector-Specific Compliance
- Finance → Basel Committee AI/ML principles → BCBS
- Finance → SEC AI/Fintech guidance → SEC
- Healthcare → HIPAA (US) → HIPAA Overview
- Healthcare → EU MDR/IVDR (AI in medical devices) → EU MDR
- Transport → ISO 26262 (functional safety for automotive) → ISO 26262
- Aviation → FAA AI/automation policies → FAA
- Aviation → EASA AI Roadmap → EASA