Key takeaways
- Governance enables AI adoption — it gives teams clear, safe ways to say “yes”.
- Start lightweight: a policy, a register, risk tiers and named owners.
- Match controls to impact: more oversight for decisions affecting people or money.
- Monitor continuously — AI systems change as data and models change.
AI governance can sound like bureaucracy, but done well it does the opposite: it gives teams the confidence to use AI quickly and safely. This responsible AI checklist is designed for business teams that need practical controls, not a hundred-page framework.
1. Policy and ownership
- Publish an acceptable-use policy covering approved tools and prohibited data.
- Name an executive owner for AI risk and a working group across business, IT, security and legal.
- Assign a business owner to every AI system.
2. AI system register
Keep a simple list of every AI system in use: purpose, owner, model or vendor, data used, users affected and risk tier. You cannot govern what you cannot see.
3. Risk tiers
| Tier | Examples | Typical controls |
|---|---|---|
| Low | Drafting internal content, summarising public information | Policy, training, spot checks |
| Medium | Customer-facing answers, internal knowledge assistants | Grounded answers, testing, monitoring, easy human escalation |
| High | Decisions affecting credit, employment, health, pricing or legal outcomes | Human approval, bias testing, explainability, audit trail, legal review |
4. Data protection
- Use only data you are permitted to use, for the stated purpose.
- Minimise personal data and mask sensitive fields where possible.
- Follow the Digital Personal Data Protection Act, 2023 and sector rules that apply to you.
- Choose deployment models that match data sensitivity — see private vs public AI.
5. Human oversight
Define where people must review, approve or be able to override AI output. Make escalation easy for both employees and customers.
6. Testing before launch
- Accuracy on realistic scenarios, including edge cases.
- Fairness checks where outcomes affect people.
- Security testing, including prompt injection for LLM systems.
- Clear acceptance criteria signed off by the business owner.
7. Transparency
Tell users when they are interacting with AI, explain what it can and cannot do, and show sources where possible.
8. Vendor review
Check data handling, retention, residency, security certifications claimed by the vendor, sub-processors and contractual commitments before onboarding AI tools.
9. Monitoring and incidents
- Track quality, drift, complaints, cost and usage.
- Review samples regularly.
- Have an incident process: pause, investigate, fix, communicate.
10. Training
Teach employees how to use AI tools well, what data they must not share and how to report issues.
For an overview of how governance fits into AI programmes, visit our AI & Automation page.
Adopt AI with confidence
We help organisations set up practical AI governance alongside their first deployments.
Frequently asked questions
What is AI governance?
AI governance is the set of policies, roles, processes and controls that ensure AI systems are used safely, legally, ethically and effectively.
Do small businesses need AI governance?
Yes, in a simple form: an acceptable-use policy, a list of AI tools in use, rules for sensitive data and a named owner are a good start.
What is human-in-the-loop?
It means a person reviews, approves or can override AI output at defined points, especially for decisions with significant impact.
How often should AI systems be reviewed?
Monitor continuously with dashboards and review formally on a regular schedule, and whenever the model, data or use case changes.
