AI Governance Fundamentals — Enterprise AI Governance

AI governance provides the framework for responsible, compliant, and ethical AI deployment across the enterprise.

What is AI Governance?

Core Components

Policy Framework: Rules governing AI development and use
Oversight Mechanisms: Review boards and approval processes
Accountability Structures: Clear ownership and responsibility
Risk Management: Identification and mitigation of AI risks
Compliance: Adherence to regulations and standards

Why Governance Matters

Prevent harmful outcomes from AI systems
Ensure regulatory compliance (EU AI Act, GDPR, etc.)
Build stakeholder trust and confidence
Manage reputational and financial risk
Enable responsible scaling of AI adoption

Governance Structure

AI Governance Board

Composition:
├── Chief AI Officer (Chair)
├── Legal / Compliance
├── Risk Management
├── Privacy / Data Protection
├── Business Unit Representatives
├── Technical AI Experts
└── Ethics Committee Members

Three Lines of Defense

First Line: Business units own AI risk
Second Line: Risk and compliance oversight
Third Line: Internal audit and assurance

AI Inventory Management

Catalog all AI systems in production
Document purpose, data sources, and decisions made
Classify by risk level (low/medium/high)
Track model versions and updates
Assign owners and review dates

Key Takeaways

•AI governance combines policy, oversight, accountability, risk management, and compliance

•Three lines of defense assign ownership across business, risk, and audit functions

•AI inventory cataloging provides visibility into all deployed systems

•Governance boards with diverse stakeholders enable balanced decision-making

AI Model Wiki