AI Governance
What Should Be in an AI Governance Policy?
The core components every established company should include in an AI governance policy before scaling AI usage across teams.
Governance should enable useful AI, not block it
A good AI governance policy gives teams permission to use AI safely. It should not be a vague warning document that scares people back into manual work.
The goal is clarity: what can be used, what cannot be shared, who approves higher-risk workflows, and how results are reviewed.
For established companies, governance matters because AI usage often spreads informally before leadership sees it. The policy brings usage into the open.
What AI governance should answer
| Question | Policy should clarify |
|---|---|
| Which tools can we use? | Approved tools and approved use cases |
| What data is restricted? | Customer, employee, legal, financial, and confidential data rules |
| Who reviews output? | Human approval requirements by risk level |
| Who approves new workflows? | Escalation path and decision owner |
| How do we document usage? | Minimum recordkeeping for important workflows |
The minimum viable policy
At minimum, define approved tools, restricted data, privacy rules, human review expectations, disclosure standards, security escalation paths, and ownership for AI-generated outputs.
The policy should also distinguish between low-risk productivity use and higher-risk workflows involving customer data, legal language, regulated information, financial decisions, or public-facing claims.
Do not start with a fifty-page policy if your team needs five pages and training. Start clear. Then improve it as real use cases appear.
Minimum viable AI policy components
| Component | What to include |
|---|---|
| Approved tools | Tools employees may use and where they may use them |
| Restricted data | Information that cannot be pasted, uploaded, or processed |
| Human review | When AI output must be checked before use |
| Disclosure | When customers, employees, or partners should know AI was used |
| Ownership | Who is responsible for final output |
| Incident path | What to do if sensitive data is exposed or output causes risk |
How to keep governance practical
AI governance should be paired with training and implementation support. If the policy says no to everything, teams will ignore it. If it gives clear examples and approved workflows, adoption gets safer and faster.
The best policy is maintained by an accountable AI owner who updates it as tools, risks, and company use cases change.
This is not a set-it-and-forget-it document. AI governance needs a cadence: review what people are using, inspect new risks, update approved tools, and retire rules that no longer help.
Practical governance rhythm
| Cadence | Action |
|---|---|
| Weekly during rollout | Review new use cases and team questions |
| Monthly | Update approved tools, workflows, and training gaps |
| Quarterly | Review vendor risk, access, incidents, and policy changes |
| After incidents | Tighten controls, document lessons, retrain affected teams |
Risk levels make the policy easier to use
Not every AI use case deserves the same level of review. Drafting an internal meeting summary is different from generating legal language, financial advice, medical content, or customer-facing claims.
A simple risk-tier model helps teams move quickly on safe use cases and slow down where judgment, compliance, or sensitive data matters.
This keeps governance from becoming a blanket no.
Simple AI risk tiers
| Risk level | Example | Required control |
|---|---|---|
| Low | Internal brainstorming or meeting summary | Approved tool and normal employee judgment |
| Medium | Customer response draft or sales proposal support | Human review before sending |
| High | Legal, financial, regulated, or public claims | Executive, legal, or compliance approval |
| Restricted | Sensitive personal data in unapproved tools | Do not use without explicit approval |
Frequently asked questions
Who should own AI governance?
AI governance should have executive ownership, often through a CAIO or cross-functional AI council with legal, security, operations, and department leaders involved.
Should employees be allowed to use public AI tools?
Often yes for low-risk tasks, but companies should define what data cannot be entered and which tools are approved for sensitive work.
What data should never go into public AI tools?
Common restricted data includes customer personal information, employee records, confidential financials, credentials, trade secrets, legal documents, health information, and proprietary strategy unless the tool and workflow are approved.
How often should an AI governance policy be updated?
Review it at least quarterly, and sooner when a new tool, workflow, incident, regulatory issue, or high-risk use case appears.
Does AI governance slow adoption?
Bad governance does. Practical governance usually speeds adoption because people know what is allowed, what requires review, and where to go with questions.
What is the first step in creating an AI policy?
Start by documenting current AI usage, approved tools, restricted data, and human review requirements. Then train teams on the policy with real examples from their work.
Next step
Find the first AI workflow your company should fix.
If your leadership team knows AI matters but does not know where to start, begin with a practical readiness audit. We will look for the workflows where AI can remove work, tighten handoffs, and create leverage.
Start with an AI readiness audit