Building Your AI GRC Program: Where to start today...

Where do you actually start?

If your organization doesn't yet have a formal AI GRC program, or has something ad hoc that doesn't quite qualify, here's the sequence that works.

Step 1: Know what you have.

Pick one primary AI risk management framework and commit to it. NIST AI RMF is the most commonly adopted in the United States. ISO 42001 is a strong choice for organizations already operating within the ISO family. The EU AI Act serves as a de facto framework for organizations with European operations. Whatever you choose, the discipline it consistently isfollowingt matters most.most.

The goal of this step is a living AI inventory that captures the purpose of each system, the data it processes, and a preliminary assessment of its risk level. This inventory also becomes your first line of defense against Shadow AI.

Step 2: Assemble the right team.

AI risk doesn't belong to IT alone, or to compliance alone, or to legal alone. It belongs to all of them. Establish a cross-functional steering committee that includes leadership from IT, cybersecurity, compliance, legal, privacy, HR, and the business units that use or plan to use AI. Give the committee a charter that defines scope and decision-making authority.

Step 3: Choose a framework.

Pick one primary AI risk management framework and commit to it. NIST AI RMF is the most commonly adopted in the United States. ISO 42001 is a strong choice for organizations already operating within the ISO family. The EU AI Act serves as a de facto framework for organizations with European operations. Whatever you choose, the discipline of following it consistently is what matters most.

Step 4: Assess your risks.

Using your framework, assess the risks associated with each system in your AI inventory. Evaluate data risks (bias in training data, data quality, privacy exposure), model risks (performance drift, lack of explainability, security vulnerabilities), and operational risks (misuse, unintended consequences, scalability failures). Work from highest risk to lowest, a risk-based approach, applied consistently.

Step 5: Develop your policies.

Translate your framework and risk assessments into internal policies and procedures. These should cover: who can approve new AI tools for use, what data can be processed by AI systems, how AI outputs are to be validated, how incidents are reported and handled, and how third-party AI risk is assessed. Publish them. Train your organization on them.

Step 6: Monitor and improve.

GRC is a continuous discipline, not a project. Build ongoing monitoring into your program. Review accepted risks on a schedule. Conduct periodic audits of AI system performance and control effectiveness. Track regulatory developments. Update policies and controls as the landscape evolves.

None of this is simple. All of it is necessary.

AI is not going away. The organizations that govern it well will be more trusted, more resilient, and better positioned to capture its benefits. The ones that don't will face the consequences, regulatory, reputational, and operational.

The path forward is the one you build intentionally, starting now.