AI Compliance and Risk Management for 2026
AI adoption is accelerating, but compliance and risk management often lag behind. This guide helps teams align model usage with regulatory requirements and organizational risk appetite.
1. Map regulations to workflows
Identify applicable regulations, industry standards, and contractual obligations. Document how each AI workflow maps to requirements before deployment.
2. Build audit evidence
Maintain model cards, data lineage, access logs, and change records. Make audit evidence retrievable within defined SLAs.
3. Assess model risk
Evaluate accuracy, bias, explainability, and failure modes. Assign risk tiers and controls based on impact and autonomy.
4. Plan incident response
Define escalation paths, communication plans, and rollback procedures. Treat model incidents with the same rigor as security incidents.
5. Review continuously
Schedule periodic compliance reviews, update controls as regulations evolve, and validate that safeguards remain effective.