What You Need to Include in an AI Policy

Artificial Intelligence (AI) tools are now part of everyday work, from content generation and inbox management to data analysis. But with that speed comes risk. And without clear guardrails, well-meaning people can end up sharing data or relying on output without the guidance they need.

That’s where a clear, practical AI policy comes in. An AI usage policy helps you embrace the benefits of these tools safely, ethically and effectively.

Why Your Business Needs an AI Policy

AI isn’t just for tech teams anymore. From HR to marketing to finance, many people are already using AI tools, often without formal training or guidance.

National survey data points the same way: the Department for Science, Innovation and Technology’s UK Business Data Survey 2026 found governance and understanding of AI regulation lagging behind adoption.

Much like social media or data handling, an AI policy is about protecting your people, your clients and your reputation.

What Is an AI Policy?

An AI policy sets expectations around how artificial intelligence tools are used across your organisation.

It should cover:

• What AI tools are permitted (and what aren’t)
• How staff can use AI safely and ethically
• How your organisation protects data, reputation and trust
• What checks and training are required before using AI in decisions

This isn’t just about locking things down. A good policy builds confidence, giving teams the freedom to experiment with the right boundaries in place.

What to Include in Your AI Policy (AI Policy Template)

Here’s a practical checklist for building the basics of your AI policy:

1. Purpose and Scope

Define why this policy exists and who it applies to. Cover all departments, not just tech or comms, and make sure it’s written in plain language, not legal jargon.

Example: “This policy outlines how staff can use AI tools to support day-to-day tasks while protecting our clients, data and brand reputation.”

2. Approved Tools

List which AI tools are approved and how they should be accessed, ideally through work accounts rather than personal or free ones, especially for tools that use cloud storage or have public-facing models.

Tip: Include a simple ‘ask before use’ route for any AI tool not on the list, so people know where to go.

3. Dos and Don’ts

Give clear examples of appropriate and risky uses. For example:

✅ You can use AI to generate content ideas or tidy the wording of a non-confidential draft

🚫 Don’t use AI to write client emails or submit grant applications without human review

4. Data Privacy & IP Protection

Make it clear that sensitive client information, financials or internal IP should never be entered into public AI tools, or into any AI tool without explicit approval. Where possible, use paid work accounts, and check approved tools do not train on your data.

CYAN Tip: We only use approved tools, through work accounts, and nothing sensitive or confidential goes into an AI tool without explicit approval.

5. Bias, Ethics and Transparency

Highlight the risk of bias in AI-generated outputs, especially for content that affects people decisions (e.g. hiring, funding, client support).

You may also choose to define whether clients need to be told when AI has shaped something you give them.

6. Human Oversight

Stress the need for human review. AI is a support tool, not a decision-maker. Make clear that the person who approves AI output remains accountable for it.

Example: “AI suggestions should be reviewed, edited or validated by a relevant team member before use.”

7. Training and Support

Let your team know where they can learn more, whether that’s internal training, a quick start guide or a point of contact.

Ongoing education is key. AI is evolving fast, and your team will need confidence to keep up.

8. Review and Updates

Set a clear schedule for reviewing your policy. Every 6 to 12 months is a sensible starting point, with an earlier review whenever a tool gains significant new features or connections. Note who is responsible for keeping it up to date.

Real-World Example: How We Use AI at CYAN

At CYAN, AI helps our team to be more productive and deliver a better service, from first drafts and summaries to data assembly and repetitive tasks. We adopt it deliberately, and people remain accountable for everything that reaches our clients.

We don’t use just any tool, or use them casually. Every tool is risk-assessed and approved before use, and used only through work accounts on paid plans, which allow:

• Secure team-wide access, instead of one person using a personal account
• Centralised control of data and outputs
• Stronger privacy settings, including assurances that company data is not used to train public AI models

AI helps us move faster, but it still needs human oversight. A person checks AI output before it is relied on or sent to a client, and we have clear internal rules about what information stays out of AI tools without explicit approval.

Creating a Safe, Smart AI Culture in Your Business

Building a clear AI policy isn’t just a tick-box exercise. It’s an essential part of using AI within an organisation in a way that’s safe, productive and trusted by your team. A policy explains the boundaries, and it works best alongside clear ownership of AI decisions and the technical controls that support it.

A simple, well-communicated policy will help your organisation:

• Stay compliant and secure
• Avoid common missteps
• Get more from AI, with human oversight built in

Need help reviewing or writing your AI policy?

We work with organisations across the UK to put the right foundations in place. If you would like to see how a policy works in practice, our AI statement in the CYAN Trust Centre sets out how we approve tools, protect data and review AI output, and how we label content where AI has helped. It isn’t a template for everyone, but it may help you decide what your own policy needs to cover.

Speak to our team about reviewing or writing your AI policy, and we’ll help you use AI safely, with structure and support.

Written by Team CYAN

Link has been copied to the clipboard.

Updated October 2026 (Based on an article published June 2025)