Navigating Enterprise Compliance, Governance, and Ethical AI

As AI moves deeper into enterprise operations, compliance and ethics become inseparable from delivery. Regulations are evolving, stakeholder expectations are rising, and the tolerance for opaque AI decisions is shrinking. Enterprise leaders need a governance model that treats AI as a controlled, auditable capability.

Know the regulatory terrain

• Privacy regulation. GDPR, CCPA/CPRA, and sector-specific mandates apply to AI data flows.
• Emerging AI laws. The EU AI Act introduces risk-based requirements for high-impact systems.
• Standards alignment. NIST AI RMF and ISO/IEC 23894 set expectations for risk management.

Build a governance structure that scales

• AI steering committee with cross-functional ownership.
• Model inventory with risk classification, owners, and evidence trails.
• Documented exception workflows for approved risk acceptance.
• Continuous monitoring for drift, bias, and policy violations.

Ethical AI is operational, not abstract

Ethical commitments must be embedded into workflows: model evaluation, access controls, audit logging, and transparency practices. This includes communicating when AI is used, documenting decisions, and providing human escalation paths for high-impact outcomes.