What is AI Governance?
AI governance encompasses the frameworks, policies, and practices for responsible development and deployment of AI systems. It provides organizations with systematic approaches to managing AI risk, ensuring fairness, maintaining transparency, and demonstrating accountability throughout the AI lifecycle.
For technology companies developing or deploying AI systems, governance is becoming a baseline expectation. Enterprise buyers require governance documentation, regulators are enacting enforceable requirements, and the public expects responsible AI practices.
Key Frameworks
Three major frameworks are shaping AI governance expectations globally:
| Framework | Scope | Approach | Status |
|---|---|---|---|
| NIST AI RMF | Voluntary, US-based | Govern, Map, Measure, Manage | Published January 2023 |
| EU AI Act | Mandatory, EU-wide | Risk-based classification (unacceptable, high, limited, minimal) | Phased enforcement 2024–2027 |
| ISO/IEC 42001 | International standard | AI management system certification | Published December 2023 |
Key Governance Areas
Regardless of which framework you adopt, AI governance addresses the same core areas:
- AI System Inventory — catalog all AI systems in development and production, including purpose, data sources, and risk level
- Risk Categorization — classify AI systems by risk level using frameworks like NIST AI RMF or EU AI Act tiers
- Bias Testing and Fairness — systematic testing for demographic bias, disparate impact, and fairness across protected classes
- Transparency and Explainability — documentation and mechanisms that make AI decisions understandable to affected individuals
- Human Oversight — defined mechanisms for human review, intervention, and override of AI decisions
- Data Governance — controls for training data quality, provenance, consent, and appropriate use
- Model Lifecycle Management — versioning, monitoring, retraining, and retirement processes for AI models
- Third-Party AI Risk — assessment and management of AI systems and models sourced from third parties
EU AI Act Risk Classification
The EU AI Act classifies AI systems into four risk tiers, each with different compliance obligations.
| Risk Level | Examples | Obligations |
|---|---|---|
| Unacceptable | Social scoring, real-time biometric surveillance | Prohibited |
| High Risk | Hiring, credit scoring, critical infrastructure | Conformity assessment, risk management, data governance, transparency, human oversight |
| Limited Risk | Chatbots, emotion recognition, deepfakes | Transparency obligations (disclosure that AI is being used) |
| Minimal Risk | Spam filters, AI-enabled games | No specific obligations (voluntary codes of conduct) |
Readiness Assessment Checklist
Before building or formalizing your AI governance program, evaluate where your organization stands against these readiness questions:
- Has an AI system inventory been completed, covering all systems in development and production?
- Has a risk categorization framework been applied to classify AI systems by risk level?
- Is there a bias testing methodology defined for high-risk AI systems?
- Is transparency documentation available for high-risk systems, including decision explanations?
- Are human oversight mechanisms defined and operational for AI systems that affect individuals?
- Is data governance in place for training and inference data, including quality and provenance controls?
If you can’t confidently answer “yes” to most of these, a readiness sprint will get you there.
Next step: See our control domain breakdown to understand what AI governance frameworks expect across all control areas, with evidence examples for each.