The biggest risk associated with AI in companies is not futuristic at all. It is the employee who pastes a client contract or source code into a free chatbot to get something done faster. The data then lands on someone else’s servers, sometimes used to train models, and the company loses control over it. The good news is that this can be put in order.

Where the real risks lie

Before you introduce rules, it is worth understanding what exactly can go wrong. The risks are concrete, not hypothetical.

  • Data leak. Data pasted into a public tool may be stored and analysed on the provider’s side.
  • Loss of trade secrets. Code, offers and strategies lose their confidential status once they go outside.
  • GDPR breach. Uploading customers’ personal data without a legal basis is a real problem.
  • Trusting wrong answers. AI can make things up with full confidence, and people buy it.

In one company a salesperson pasted the entire contact database into a chatbot to sort it nicely. Technically nothing broke. Legally, the company had just handed personal data to a third party without any agreement. That is not a failure, it is a decision no one anticipated in the rules.

Step 1: decide which tools may be used

Banning AI does not work, because people will reach for it from their private phone anyway. It is better to give a safe alternative and say clearly what is allowed.

  • Point to specific company tools, ideally in the business version, where data is not used to train models.
  • Distinguish approved tools from public, free ones that must not be used with company data.
  • Ensure sign-in with a company account so it can be managed.

Step 2: define what must not be uploaded

This is the core of the whole policy. People need a simple, memorable rule. A good one is: if you would not send it on a postcard, do not paste it into AI.

A list of data that should not go into public tools:

  • Personal data of customers and employees.
  • Contracts, offers and financial data.
  • Source code and technical documentation.
  • Passwords, keys and access credentials, never and nowhere.

Step 3: back the rules up technically

Policy alone is not enough, because people make mistakes. It is worth supporting it with tools that catch risky behaviour before the data goes outside.

This is where the DLP class of solutions comes in, that is, data leak protection. It can detect and block the sending of sensitive content, including to AI tools in the browser. If you process a lot of personal data or trade secrets, take a look at our DLP data protection service. It is the layer that watches over the rules when a person forgets.

What else helps

  • Restricting access to AI tools to the company versions through browser and identity settings.
  • Confidentiality labels on documents, so they can be controlled.
  • Monitoring of unusual behaviour, for example mass data exports.

Step 4: train the team and do it regularly

The best protection is an aware employee. A short, concrete training session with examples from your industry works better than a long set of rules. Show what is allowed, what is not and why. Repeat it, because tools and temptations change quickly.

Summary

Using AI safely is not a ban, it is a framework. You give the team good tools, a clear rule on what not to upload, technical support in case of a mistake and a regular reminder. That is how you get the benefits of AI without exposing the company to a leak.

If you want to deploy AI with security in mind from day one, take a look at our AI implementation for business. We arrange tools, rules and data protection so they all pull in the same direction.