AI governance and compliance is about establishing the policies, procedures, and oversight mechanisms to ensure AI systems are developed and used responsibly. As AI adoption grows, so does regulatory scrutiny and the need for robust governance frameworks.
The AI regulatory landscape is evolving rapidly. Organizations using AI must navigate a complex patchwork of regulations that vary by jurisdiction, industry, and use case. Compliance is no longer optional—it’s becoming a legal requirement and a business imperative.
The world’s first comprehensive AI law, with phased application starting 2024.
Risk-Based Classification:
| Risk Level | Description | Examples | Requirements |
|---|
| Unacceptable | Prohibited | Social scoring, real-time biometric ID in public spaces (narrow law-enforcement exceptions) | Prohibited |
| High | Strict obligations | Critical infrastructure, hiring, medical devices | Conformity assessment, risk management, data governance, logging, human oversight, accuracy/robustness/cybersecurity, CE marking (where applicable) |
| Limited | Transparency obligations | Chatbots, AI-generated content/deepfake labeling | Users must be informed they’re interacting with AI; AI-generated content must be labeled |
| Minimal | No restrictions | Spam filters, video games | No specific obligations (existing laws apply) |
Key deadlines:
- Feb 2025: Prohibited practices apply
- Aug 2025: GPAI obligations start applying
- Aug 2026: Most high-risk obligations apply
- Aug 2027: Some high-risk product/safety-component cases apply later
EU’s General Data Protection Regulation intersects with AI in several areas:
| Aspect | Requirement |
|---|
| Automated decision-making | Restrictions/safeguards on solely automated significant decisions (Art. 22), incl. ability to seek human intervention/contest |
| Data minimization | Use only necessary data for AI training |
| Purpose limitation | Train for specific, legitimate purposes |
| Individual rights | Data subject rights may apply to personal data used in training; operationalizing deletion/unlearning is complex |
| Data portability | Right to transfer data to another service |
Directs federal agencies to:
| Area | Action |
|---|
| Standards | NIST to develop AI safety and security standards |
| Reporting | Directs agencies to establish reporting requirements for certain advanced AI models/compute clusters (scope depends on final rules) |
| Guidance | Sector-specific guidance for AI use in critical infrastructure |
| Talent | Attract and retain AI talent in government |
| Region | Status | Key Framework |
|---|
| UK | Pro-innovation approach | Voluntary codes of practice |
| China | Comprehensive regulations | Recommendation + deep synthesis + generative AI measures |
| Canada | No federal AI Act yet | Voluntary code + privacy/provincial rules |
| Singapore | Model AI Governance Framework | AI Verify foundation |
AI governance is becoming a board-level responsibility.
| Consideration | Questions to Ask |
|---|
| Accountability | Who is ultimately responsible for AI outcomes? |
| Expertise | Does the board have sufficient AI literacy? |
| Oversight | How are AI decisions reviewed and challenged? |
| Risk tolerance | What level of AI risk is acceptable? |
Effective AI governance typically involves:
| Component | Description |
|---|
| Steering committee | Cross-functional group making AI policy decisions |
| Responsible AI office | Center of expertise for ethical AI practices |
| Review boards | Technical and ethical review of AI projects |
| Subject matter experts | Legal, ethical, technical advisors |
| Role | Responsibilities |
|---|
| Executive sponsor | Overall accountability for AI initiatives |
| AI Ethics Committee | Review and approve high-risk projects |
| Data stewards | Ensure data quality and appropriate use |
| Model owners | Responsible for specific models in production |
| Compliance officer | Ensure adherence to regulations and policies |
Four-part continuous cycle:
| Phase | Activities |
|---|
| Govern | Establish policies, procedures, and oversight |
| Map | Identify AI systems, categorize risks, set tolerance |
| Measure | Assess systems against mapped risks |
| Manage | Respond to risks with appropriate controls |
First international standard for AI management systems.
| Focus Area | Key Requirements |
|---|
| Leadership and commitment | Executive buy-in, AI policy |
| Planning | AI impact assessment, risk management |
| Risk assessment | Systematic evaluation of AI risks |
| Controls | Implementing controls to address risks |
| Information sharing | Documenting and communicating AI risks |
| Continuous improvement | Monitoring and enhancing AI management system |
| Risk Category | Examples | Mitigation |
|---|
| Reputational | AI makes offensive statements | Content filtering, review processes |
| Legal | Copyright infringement, privacy violations | Legal review, data governance |
| Operational | System failure, downtime | Monitoring, fallback mechanisms |
| Financial | Fines, penalties, remediation costs | Compliance programs, insurance |
| Ethical | Unfair treatment, discrimination | Bias testing, impact assessments |
- Identify AI systems in use
- Categorize by risk level and application domain
- Assess likelihood and impact of potential harms
- Mitigate with appropriate controls
- Monitor for new risks and effectiveness of controls
| Document Type | Purpose |
|---|
| AI inventory | Track all AI systems in use |
| Model cards | Document model capabilities, limitations, intended use |
| Impact assessments | Assess potential impacts before deployment |
| Risk assessments | Evaluate and document risk mitigation measures |
| Policies | Clear guidelines for acceptable AI use |
| Training records | Evidence of staff training on AI ethics |
| Activity | Frequency |
|---|
| Model performance monitoring | Continuous |
| Bias audits | Quarterly or after significant updates |
| Security testing | Regular, especially for high-risk systems |
| Legal review | Before major deployments, when regulations change |
| Stakeholder feedback | Ongoing mechanisms for affected communities |
| Incident response | As needed, with post-incident reviews |
| Pitfall | Consequence | Prevention |
|---|
| Waiting until regulations are final | Rushing compliance, catching up later | Start now, use frameworks as guides |
| Focusing only on technology | Neglecting process and people | Take holistic approach |
| One-time compliance | Systems drift out of compliance | Continuous monitoring and updates |
| Ignoring international standards | Market access limitations | Consider global requirements from the start |
- Regulatory landscape is evolving: EU AI Act is the first comprehensive AI law, with other jurisdictions following
- Risk-based approach: Most regulations use risk tiers (unacceptable, high, limited, minimal)
- Corporate governance is essential: Board-level oversight, clear roles, accountability
- Use established frameworks: NIST AI RMF, ISO/IEC 42001, and others provide structure
- Compliance is ongoing: Not a one-time project but continuous process
- Documentation is critical: Inventories, assessments, policies, and records are essential
- Start now: Even if regulations aren’t final in your jurisdiction, begin preparing