Building an AI governance program
A governance program turns principles into routine decisions. The exam favors programs that are cross-functional, risk-based and built on what the organization already has.
People and roles
- Board and executive sponsor: set risk appetite and fund the program.
- AI governance committee or council: cross-functional (legal, privacy, security, data science, product, HR, ethics, procurement, business owners). Approves high-risk uses and sets policy.
- System owner: a named, accountable business owner for each AI system.
- Three lines of defense: 1st line builds and runs AI; 2nd line (risk, compliance, privacy) sets standards and challenges; 3rd line (internal audit) gives independent assurance.
Core building blocks
| Building block | Purpose |
|---|---|
| AI policy and acceptable-use policy | What is allowed, prohibited and needs approval |
| AI inventory / register | Every system: owner, purpose, risk tier, data, vendor, review date |
| Risk classification method | Sorts uses into tiers that set the level of review |
| Impact assessments | AIA, DPIA, FRIA as required |
| Lifecycle gates | Approvals before build, before release, at re-review |
| Training and awareness | A program for everyone, pitched by role: shared terms for all staff, deeper training for builders, reviewers and decision-makers. The EU AI Act (Art. 4) has expected AI literacy since Feb 2025; the 2026 AI Omnibus softened it to supporting staff literacy |
| Third-party and HR policies | AI terms in procurement and vendor contracts; HR rules on AI in hiring and in monitoring staff, and on staff using AI at work |
| Metrics and reporting | Show the board how risk is being managed |
Build on what exists
Most organizations already have privacy reviews, security reviews, model-risk management (for example SR 11-7 in US banking), vendor management and ethics committees. Extending these is faster and better integrated than building a separate AI bureaucracy. That is frequently the "best" answer.
Proportionality
A low-risk internal tool should get a light review. A system that makes decisions about people's jobs, credit or health gets full assessment, testing and oversight. Treating everything as high risk wastes effort and pushes people to work around the program.
The organization matters as much as the system. Size, maturity, sector, what it sells and how much risk it will accept all change the right design: a startup might run governance through one accountable owner and a checklist, while a bank folds AI into the model-risk management it already has. Whether it builds AI, buys it, or both changes the duties too.
Check your understanding
What should an AI inventory record for each system at minimum?
A company with mature privacy and security review processes is starting AI governance. What is the BEST approach?