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Nate Patel
Nate Patel

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Building Your AI Governance Foundation

AI governance isn’t a future luxury—it’s today’s survival kit. Before regulations lock in and risks snowball, lay down a pragmatic framework that inventories every model, assigns accountable owners, embeds proven standards (NIST, ISO/IEC 42001), and hard-wires continuous monitoring. 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

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