AI Governance: 10 Best Practices for 2026

This Luminova blog offers up the following list of 10 AI governance best practices for 2026:

1. Establish a Cross-Functional AI Governance Committee
2. Define Clear AI Use Case Approval and Risk Classification Workflows
3. Align Governance with Global Regulations (EU AI Act, U.S. Executive Orders)
4. Maintain a Centralized AI System and Policy Repository
5. Operationalize Risk & Compliance Testing with Configurable Templates
6. Monitor Models Continuously Post-Deployment
7. Quantify AI Risk and Make It Actionable for Decision-Makers
8. Enable Traceability and Explainability Across the AI Lifecycle
9. Automate Alerts, Escalations, and Risk Mitigation Workflows
10. Foster a Responsible AI Culture Through Training and Communication

The blog goes on to identify some specific actions that should be taken with respect to each of the identified best practices. For example, here’s what the blog says when it comes to post-deployment monitoring of models:

The “set it and forget it” mentality is dangerous. A model that is safe on Day 1 can drift on Day 30 due to changing data patterns or adversarial attacks.

Best practice: Shift from point-in-time assessments to continuous monitoring. You need real-time visibility into model performance, data drift, and fairness metrics.

Key action: Set up automated thresholds. If a credit risk model’s denial rate for a specific demographic spikes, your risk team should know immediately – not next quarter.