AI Governance: What It Is & How to Implement It

Summary

This Diligent Corporation guide cuts through the AI governance complexity by focusing on what C-suite executives and board members actually need to know. Rather than diving into technical weeds, it addresses the critical question: how do you create governance structures that protect your organization from AI risks while enabling innovation? The resource emphasizes the intersection of AI governance with existing corporate governance frameworks, making it particularly valuable for organizations that want to integrate AI oversight into their current board and executive processes rather than building parallel structures.

Who this resource is for

• Chief Officers (CEO, CTO, CISO, CDO) who need to present AI governance strategies to their boards
• Board members seeking practical frameworks for AI oversight without requiring deep technical expertise
• Corporate governance professionals tasked with extending existing governance structures to cover AI systems
• Risk management teams in mid-to-large enterprises deploying or planning AI implementations
• Legal and compliance officers working on AI policy development within established corporate frameworks

The Corporate Governance Angle

What sets this guide apart is its focus on fitting AI governance into existing corporate structures rather than creating entirely new ones. Diligent's approach recognizes that most organizations already have risk committees, audit processes, and governance frameworks—the challenge is adapting these for AI-specific risks like algorithmic bias, data poisoning, and model drift.

The resource emphasizes board-level oversight of AI initiatives, treating AI governance as an extension of corporate governance rather than a separate IT concern. This perspective is particularly valuable for publicly traded companies where board accountability for AI risks is becoming a shareholder and regulatory expectation.

Implementation Roadmap

The guide provides a phased approach that starts with governance structure before diving into technical controls:

Phase 1: Governance Foundation

• Establish AI oversight responsibilities at the board level
• Create or adapt existing committees to include AI risk oversight
• Define roles between technical teams and governance bodies

Phase 2: Risk Framework Integration

• Map AI risks to existing enterprise risk categories
• Develop AI-specific risk appetite statements
• Create escalation paths from technical teams to executive leadership

Phase 3: Operational Controls

• Implement monitoring and reporting mechanisms
• Establish incident response procedures for AI system failures
• Create ongoing compliance and audit processes

Security-First Perspective

Unlike many AI governance resources that treat security as one of many considerations, this guide positions cybersecurity and data protection as foundational elements. It addresses specific threats that AI systems create—like expanded attack surfaces through training data vulnerabilities and the potential for adversarial attacks that could compromise business operations.

The security focus includes practical guidance on protecting AI training data, securing model deployment environments, and managing third-party AI service risks—critical considerations often overlooked in more academic governance frameworks.

Watch out for

While comprehensive from a governance perspective, this resource has some limitations:

• Industry specificity: The guidance is fairly generic and may need significant adaptation for heavily regulated industries like healthcare or financial services
• Technical depth: Board-focused approach means it lacks detailed technical implementation guidance that IT teams will need
• Vendor perspective: As a Diligent publication, it naturally emphasizes board management solutions—consider supplementing with vendor-neutral resources
• Regulatory coverage: Global scope means it doesn't dive deep into specific regulatory requirements in particular jurisdictions