A useful AI policy clearly states which data and tools are allowed, which uses need approval, who is accountable and how to report an incident. It should be short, illustrated and updated with practice.
AI governance: building a simple, usable policy
Data, tools, approval, responsibilities and incidents: minimum viable governance. Reliable results require connecting technology to a workflow, data, an owner and a measure. The following principles structure that decision.
01 — Classify data as public, internal, confidential, personal and highly sensitive
Classify data as public, internal, confidential, personal and highly sensitive.
02 — Maintain an approved tool list with accounts, settings and conditions
Maintain an approved tool list with accounts, settings and conditions.
03 — Define prohibited, assisted and autonomous uses
Define prohibited, assisted and autonomous uses.
04 — Assign owners to the workflow, data and solution
Assign owners to the workflow, data and solution.
05 — Plan reporting, analysis, correction and incident communication
Plan reporting, analysis, correction and incident communication.
Action plan
Use this sequence as a starting point. Each step should produce a decision or verifiable output before the next.
- Map usage
- Classify data
- Approve tools
- Define roles
- Train teams
- Review quarterly
Mistakes to avoid
- Writing a legal-only policy
- Banning without alternatives
- Never updating policy
Frequently asked questions
Leadership sets risk appetite; business, IT, security, legal and HR then share clear responsibilities.
Yes, proportionally: data rules, approved tools, review requirements and a contact for questions.
Key takeaway
A useful AI policy clearly states which data and tools are allowed, which uses need approval, who is accountable and how to report an incident. It should be short, illustrated and updated with practice.
The important point is to progress through evidence: a precise use case, representative test, documented limits and an outcome-based decision.