Building Your AI Governance Foundation | Nate Patel

The action plan below shows how to move from scattered experiments to a disciplined, risk-tiered governance foundation—fast.

Waiting for perfect regulations or tools is a recipe for falling behind. Start pragmatic, start now, and scale intelligently.

Key Steps:

1. Audit & Risk-Assess Existing AI: Don't fly blind.

   • Inventory: Catalog all AI/ML systems in use or development (including "shadow IT" and vendor-provided AI).

   • Risk Tiering: Classify each system based on potential impact using frameworks like the EU AI Act categories (Unacceptable, High, Limited, Minimal Risk). Focus first on High-Risk applications (e.g., HR, lending, healthcare, critical infrastructure, law enforcement). What's the potential harm if it fails (bias, safety, security, financial)?

2. Assign Clear Ownership & Structure: Governance fails without accountability.

   • Establish an AI Governance Council: A cross-functional team is non-negotiable. Include senior leaders from:

     • Legal & Compliance: Regulatory navigation, contractual risks.

     • Technology/Data Science: Technical implementation, tooling, model development standards.

     • Ethics/Responsible AI Office: Championing fairness, societal impact, ethical frameworks.

     • Risk Management: Holistic risk assessment and mitigation.

     • Business Unit Leaders: Ensuring governance supports business objectives and usability.

     • Privacy: Data protection compliance.

   • Define Roles: Clearly articulate responsibilities for the Council, individual AI project owners, data stewards, model validators, and monitoring teams. Empower the Council with authority.

Read More: Building Your AI Governance Foundation