In this article, we will explore the five must-have elements of an internal AI policy and why they matter for compliance, productivity, and trust.
1. Purpose and Scope
A good AI policy starts by defining its purpose: why the organisation is introducing rules around AI use. This might include:
- Supporting responsible innovation.
- Protecting sensitive data.
- Complying with regulations like the EU AI Act.
It should also outline the scope: which employees, departments, or tools the policy applies to. Without this, teams may assume the rules don’t apply to them.
✅ Best practice: Keep the language simple so everyone understands it, not just compliance or IT staff.
2. Approved (and Prohibited) Tools
One of the biggest risks today is “shadow AI”, employees signing up for unapproved apps. To prevent this, your policy should include an allowlist of approved AI tools and where needed, a denylist of banned applications.
This helps staff know:
- Which AI platforms are safe to use.
- Which tools carry risks and should be avoided.
✅ Best practice: Keep this list updated, as new AI tools emerge every month. Survey employees what they use and ban unwanted AI apps.
3. Data Protection and Confidentiality
AI models are only as safe as the data you put into them. That’s why policies must set rules for what employees can and cannot share with AI systems.
Clear rules should cover:
- No entry of personal or sensitive data.
- Restrictions on client information or intellectual property.
- Guidance on anonymising inputs before using AI.
✅ Best practice: Link your AI policy with existing GDPR or data privacy policies for consistency.
4. Human Oversight and Accountability
AI tools can generate impressive results, but they also make mistakes, sometimes confidently. An internal policy must stress the importance of human oversight:
- Employees remain responsible for outputs, even when AI is used.
- All AI-generated content should be checked for accuracy, bias, and appropriateness.
- Certain decisions (e.g. hiring or compliance reporting) should never be left to AI alone.
✅ Best practice: Make oversight role-specific, different departments may need different review processes.
5. Training and Continuous Learning
An AI policy is not just about restrictions, it is also about building confidence and literacy. Employees need training to understand both the opportunities and the risks.
This can include:
- Short e-learning modules on safe AI use.
- Quizzes to test comprehension.
- Access to a prompt library with safe, pre-approved examples.
✅ Best practice: Review training regularly, as regulations and AI tools evolve quickly.
Making Your Policy Operational
Writing an AI policy is only the first step. To make it effective, organisations should:
- Share it widely across teams.
- Embed it in onboarding for new employees.
- Provide easy access through a central hub (not just a forgotten PDF).
At Oregani try to make AI Policies accessible to employees to strenghtne training and AI educatoin.