Private language models and company data security

Where does your data go in public AI tools?

When an employee pastes a fragment of a contract or a customer's data into a public AI chat, that information leaves the company. It ends up on an external vendor's servers, often outside Europe, and the company loses control over it.

The problem rarely comes from bad intentions. Employees use public tools because they're at hand and they help with the work. Without a company alternative, bans simply don't work; the data leaks out anyway, just quietly.

What is a private language model?

A private language model is AI that runs on your own infrastructure: on a server sitting in your office, or in a dedicated, private cloud. The data doesn't go outside, the model processes it locally and stores the results locally.

Modern open models achieve a quality that's sufficient for most business tasks: analyzing documents, answering questions from a company knowledge base, drafting letters and summaries. You don't need the biggest model in the world; you need one that's good enough for your tasks, and one you're in control of.

What does this change in terms of GDPR and trade secrets?

From a GDPR perspective, the difference is fundamental: with a local model there is no transfer of personal data to an external party, so the whole layer of data-processing agreements, analyses of transfers outside the EEA, and dependence on a vendor's privacy policy disappears.

For management boards and data protection officers, this is an argument that usually ends the discussion of "can we use AI" and turns it into a conversation about "what should we use AI for first".

Which companies really need private AI?

If your company works with customer data, contracts, medical records or proprietary technical documentation, a private model should be your starting point. This applies especially to law firms, accounting offices, medical businesses, and manufacturers with their own know-how.

If you only process public and marketing data, public tools may be enough. In practice, most of our clients choose a mixed model: private AI for sensitive data, public tools for creative tasks that involve no company data.

Does a private model require your own server room?

Not always. There are two routes: hardware in your own office (full physical control, a one-off investment) or a dedicated private cloud (no hardware purchase, data still isolated). The choice depends on scale, budget and industry requirements.

Importantly, you don't need an IT department to get started. The implementation covers configuration, connecting your company's knowledge sources, and training the team. We handle the maintenance ourselves, or hand it over to a person designated by the company.

Where to start if sensitive data is blocking automation?

Start with an inventory: which processes touch sensitive data and which do not. It often turns out that part of the automation (e.g. internal reports, document workflows) can be implemented right away, while a private model is only needed for a narrower scope, such as contract analysis or customer service involving personal data.

A map like this lets you move quickly where there is no risk, and consciously plan the investment in private AI where it is truly necessary.

Frequently asked questions