What an AI Safety Policy Covers

An AI safety policy isn't just a document for compliance folders. It's a shared understanding of what's allowed, what isn't, and who's responsible when AI is involved in work. When it's done well, it removes uncertainty, reduces quiet risk-taking, and gives your team confidence that they're using AI in ways that help the business rather than exposing it.

A good policy covers which AI tools are approved, what data can be used with them, how AI outputs should be checked, how incidents are reported, and how often practices are reviewed. It also sets expectations around training and accountability, so AI use doesn't drift into informal habits that nobody fully owns.

If you've ever heard someone say ‘I wasn't sure if I was allowed to put that into ChatGPT, but I needed an answer quickly', that's exactly the gap a policy closes.

Step-by-Step: Writing Your AI Safety Policy

The steps below follow a practical order. You'll start by understanding how AI is already being used, then decide what level of risk your business can tolerate, and finally put clear, workable controls in place. You don't need to implement everything at once, but working through these stages will give you a policy that fits your business rather than a generic template that sits untouched.

This process complements other business security measures. If you haven't already created a cybersecurity policy, the steps here will overlap with that work in useful ways.

Step 1: Assess your current AI usage

Before writing any rules, you need to know how AI is already being used. In most organisations, AI arrived quietly. Someone tested a tool. Others followed. Now it's part of daily work, even if no one formally approved it.

Start by asking your team what tools they're using. Include obvious tools like ChatGPT or Copilot, but also AI features built into software you already pay for, such as email platforms, document editors, or CRM systems. According to Gartner, 55% of organisations are using generative AI, yet many still lack visibility over which tools staff rely on. That lack of visibility is where risk begins.

Once you know the tools, look at how they're being used and what data goes into them. Client details, internal documents, contracts, financial data, strategy notes. If information matters to your business, it needs protection. This mapping step becomes the foundation of your policy, and it's often the moment businesses realise how much AI use has grown without oversight.

Step 2: Define your risk tolerance

Not all businesses face the same AI risks, so your policy needs to reflect your reality rather than someone else's. A company handling sensitive personal data will need stricter controls than one working mostly with public information. A business with proprietary methods or valuable client lists has more to lose from accidental exposure. And industries built on trust, like finance, legal services, healthcare, or education, can't afford visible mistakes.

This step is about deciding where you're willing to accept low-level risk and where you aren't. Be realistic. A policy that assumes zero risk and perfect behaviour won't survive contact with daily work, and in my experience teams disengage quickly when rules feel impossible to follow. It's better to set clear priorities, protect what truly matters, and create controls you can actually maintain.

It also helps to consider your resources. If you're a small team, you'll want simple processes that fit into existing workflows. Overly complex approval chains and monitoring systems usually get bypassed, even when intentions are good. Understanding how small businesses get scammed online can help you identify which risks deserve the most attention.

Step 3: Establish approved AI tools

Your policy needs a clear list of which AI tools are approved for business use and under what conditions. Without this, staff will default to whatever is easiest to access, which often means free consumer tools with limited data protections.

When evaluating tools, look at how they handle data, what they store, and whether they use your inputs to train their models. Enterprise versions often offer stronger privacy controls, audit logs, and clearer contractual protections, which makes a significant difference when something goes wrong.

It's also important to specify what each tool can be used for. For example, a tool might be approved for drafting internal notes but not for processing client data. Another might be suitable for coding assistance but require code review before deployment. These distinctions remove guesswork and help people make good decisions without needing constant approval, because most people just want to get the work done without having to second-guess every step.

Finally, include a process for requesting new tools. AI evolves quickly, and your policy should allow room to adopt useful tools while keeping oversight in place.

What worked for us is keeping the approved tool list short and intentional. When the list becomes a shopping catalogue, people stop reading it and go back to whatever's quickest.

Step 4: Create data classification rules

Most AI-related data incidents happen because people aren't sure what they're allowed to share. They aren't being reckless. They're filling a gap with their best judgement. A data classification system removes that uncertainty.

You don't need an enterprise-grade framework. Simple categories work well. For example, public information that's safe to use anywhere. Internal information that can be used only with approved tools. Confidential information that needs extra safeguards. And prohibited information that should never be entered into AI tools.

The key is clarity. ‘Confidential information' is vague. ‘Customer contact details, employee records, financial statements, contracts, proprietary processes' is clear. The more specific you are, the easier it is for staff to follow the rules without hesitation.

According to IBM's Cost of a Data Breach Report 2024, the global average cost of a data breach reached £3.6 million. That figure sounds enormous, but even a small incident can create serious disruption for a growing business. Clear data rules are one of the simplest ways to prevent accidental exposure through AI tools.

We're still figuring out the perfect way to explain data rules without turning them into a policy novel, but the simplest version has held up best: if you wouldn't put it in an email to a stranger, don't put it into an AI tool.

Step 5: Set output verification requirements

AI tools make mistakes more often than many people realise. They hallucinate facts, invent sources, and deliver answers with confidence that can feel reassuring. The tricky part is that they often sound right, even when they're wrong.

Your policy needs to define how AI outputs are checked before they're used. Not every output requires the same level of scrutiny. Internal brainstorming notes might only need a quick scan. Client-facing emails, financial analysis, legal clauses, or public-facing content need proper review. Someone still owns the final output. AI doesn't.

It also helps to be explicit about what verification looks like. Sometimes it's fact-checking against source documents. Sometimes it's checking tone and brand alignment. Sometimes it's testing functionality. When you spell this out, people stop guessing what ‘review' means and start applying it consistently, which is usually when quality improves without the process feeling heavier.

With the rise of AI-generated impersonations, verifying the authenticity of communications from clients and suppliers has become increasingly important alongside verifying your own AI outputs.

This is the one had to learn the hard way too. AI often sounds right, and when you're moving fast it's easy to let that confidence do the convincing. Having a ‘second set of eyes' rule for anything client-facing has saved us from avoidable embarrassment more than once.

Step 6: Address bias and fairness

We already know AI systems can amplify existing bias, and that becomes a business risk when AI influences decisions about people. Recruitment, customer service, credit decisions, performance assessments, and access to services all fall into this category.

Your policy should require human oversight for high-stakes decisions. AI can support, but it shouldn't be the final decision-maker where fairness, legality, or reputational risk is involved. Saying ‘the system decided' won't protect a business if harm occurs.

It's also worth building in periodic checks for patterns that don't feel right. Reviewing samples of decisions, looking for consistent disparities, and giving people a way to question AI-assisted outcomes helps catch problems early.

Step 7: Define incident reporting

Even with good controls, things can go wrong. Someone might enter data they shouldn't. A tool might behave unexpectedly. An AI-generated message might go out with an error that matters.

Your policy should explain what counts as an AI-related incident and how it's reported. Make reporting straightforward. A named contact. A shared inbox. A simple internal channel. When raising a concern feels easy, people speak up sooner, and when people speak up sooner, small problems stay small.

It also helps to outline what happens next, so staff know they won't be blamed for flagging issues. Early visibility is one of the most effective risk controls you can have, and it's often the difference between a quiet fix internally and a problem that spreads beyond your control.

Step 8: Training and culture

A policy only works if people understand it. Training doesn't need to be complicated, but it should explain what's expected, why it matters, and how to apply the rules in everyday situations.

Use examples that feel familiar. A client email drafted with AI. A spreadsheet summary generated by a tool. A rushed moment when someone is tempted to paste sensitive data for convenience. When people recognise themselves in scenarios, guidance sticks.

Culture matters too. If your environment rewards speed over care, people will take shortcuts. If you make it clear that safe AI use is valued, supported, and expected, behaviour follows, and you'll notice people get more careful and more confident when they know asking ‘is this allowed?' won't be treated like a silly question.

Practical training on how to spot AI-powered phishing and deepfake scams should form part of your broader AI safety training programme, as these threats continue to evolve.

One of the most effective shifts we've seen is when businesses stop treating AI safety as a compliance task and start treating it as part of everyday professionalism. When people feel trusted and informed rather than policed, safe behaviour becomes natural.

Step 9: Review and improve

AI changes quickly, and your policy can't be static. Build in regular review points to check whether tools are still appropriate, whether rules still make sense, and whether new risks have appeared.

Regulation is evolving too. The EU AI Act, which came into force in 2024, introduced risk-based requirements for AI systems, with stricter obligations for high-risk uses around transparency, accuracy, and human oversight. Even if you're not directly regulated today, this direction of travel is clear, and building sensible governance now is far easier than reacting under pressure later, because once you're in firefighting mode, you're making rushed decisions when you can least afford them.

Feedback from your team is valuable here. If people are confused, bypassing steps, or unsure how to apply rules, your policy needs refinement. A policy that looks good on paper but doesn't work in practice won't protect you.

Making your policy work in real life

The most effective AI safety policies are written in plain language, easy to access, and clearly owned. They don't live in forgotten folders. They're referenced, discussed, and updated as part of normal operations.

Support your policy with practical alternatives. If you restrict certain tools, provide approved ones that meet genuine business needs. If you require output verification, make sure people have time to do it properly. Safe behaviour is much easier when the environment supports it.

AI safety isn't about slowing your business down. It's about using powerful tools with awareness, structure, and responsibility. When you get this right, you reduce risk, protect trust, and create a foundation that lets you adopt AI with confidence rather than caution.