Direct answer
An AI governance program is an operating model, not a document — a cross-functional capability that can evaluate, approve, monitor, and retire AI systems. Start with your highest-risk systems, assign clear ownership, and align to recognized frameworks: the NIST AI RMF, ISO 42001, and the EU AI Act, with SOC 2 evidence where customers require it.
The roles you need
- An accountable owner — an AI governance officer or chief AI ethics officer who owns outcomes, not just paperwork.
- A review board — cross-functional: legal, privacy, security, risk, business, engineering, and domain experts.
- A model-risk function — to tier systems by risk and set the depth of review each tier requires.
The frameworks, and how they fit
| Framework | What it is | Use it for |
|---|---|---|
| NIST AI RMF | Voluntary risk framework | Building risk-management muscle |
| ISO 42001 | Certifiable management standard | External assurance & competitive advantage |
| EU AI Act | Binding law | Legal compliance if you touch the EU |
| SOC 2 | Security/availability attestation | Evidence customers and procurement ask for |
Getting started without boiling the ocean
Inventory where AI is already in use (including shadow tools), tier those systems by risk, assign a named owner to each, and apply heavier review only where the stakes are highest. Build incrementally — governance that emerges from real use is more durable than a framework designed in a vacuum.
AI Guru advises boards and teams on exactly this, and can produce SOC 2 evidence via AssuranceOps. See our governance practice.