The EU AI Act is now in force. The NIST AI RMF has become the de facto US standard. Most regulated industries have issued AI-specific guidance. Enterprises that haven't started building AI governance frameworks are already behind on compliance.
Key regulatory frameworks (2026)
| Framework | Jurisdiction | Key Requirements | Applies to |
|---|---|---|---|
| EU AI Act | European Union | Risk classification, conformity assessment, transparency obligations | Any AI deployed in EU |
| NIST AI RMF | United States | Govern, Map, Measure, Manage lifecycle | US federal agencies; de facto enterprise standard |
| ISO 42001 | Global | AI management system, impact assessments | Any certified organization |
| SR 11-7 | US Banking | Model risk management, validation, documentation | US financial institutions |
Five pillars of enterprise AI governance
1. AI Inventory & Risk Classification
Maintain a registry of every AI system in production. Classify each by risk level (high/medium/low) based on impact to individuals and reversibility of decisions. Determines oversight requirements for each system.
2. Access Controls & Data Governance
Define who can deploy, modify, or query AI systems. Enforce data minimization — send only necessary data to external model providers. Maintain data lineage for all training and fine-tuning data.
3. Audit Logging & Explainability
Log every AI call: input, output, model version, latency, cost. High-risk decisions require explainability — the model's reasoning must be reconstructible for audit or challenge. Immutable audit trails are non-negotiable in finance and healthcare.
4. Bias & Fairness Monitoring
Monitor output distributions by demographic group for any AI system making (or informing) consequential decisions. Automated bias detection with alerting when disparate impact exceeds defined thresholds.
5. Model Change Management
Treat every model version upgrade as a change requiring validation, not just a software deployment. Model providers change outputs without notice — automated regression testing on every model version change is essential.
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