A practical AI governance framework covering ownership, AI inventory, policy, risk assessment, vendor review, human oversight and incident management.
What AI governance actually needs to achieve
AI governance is the set of decisions, responsibilities and controls that determine how an organisation selects, uses, monitors and changes AI. It should make responsible use easier, not bury teams in paperwork.
A good framework lets a business answer basic questions quickly: what AI is in use, who owns each use case, what information is involved, what could go wrong, how people oversee outputs and what happens when something changes or fails.
Six building blocks
- Ownership: assign a business owner for AI governance and an accountable owner for material use cases.
- Inventory: maintain a living record of AI tools, systems, features and important use cases.
- Rules: define acceptable use, restricted information, approval triggers and human verification expectations.
- Risk review: screen higher-impact uses more deeply and document why controls are proportionate.
- Third parties: assess providers, contractual information, data handling and available system documentation.
- Monitoring: create change, incident, escalation and periodic review processes.
Keep governance proportional
An internal writing assistant should not automatically receive the same review as a system influencing recruitment, credit or access to essential services. Use consistent triage criteria, then increase the depth of assessment when potential impact, uncertainty or regulatory relevance increases.
Proportionality also makes adoption easier. Teams are more likely to report new AI tools when the process is clear and reasonably quick for low-risk use.
What businesses should check
- Can someone produce a current list of approved AI tools and important use cases?
- Do employees know which information must not be placed into public AI tools?
- Is there a documented review before AI affects people or important decisions?
- Are supplier claims checked rather than accepted at face value?
- Can an employee report an AI error, unexpected output or misuse without guessing who owns the issue?
Blanche perspective
Start with visible ownership, an inventory and a small number of decision rules. Those three elements create the operating spine for policy, risk assessment and regulatory readiness, and they can mature as the organisation's AI use grows.
Sources
Primary and authoritative sources used for this Insight.