Quick answer: The contract, customer list or source code an employee pastes into an AI chatbot is a data leak that leaves the organization's control. A cloud based assistant processes your text on its own servers; depending on the provider's terms, retention period and whether it is used to train the model, that information can end up in others' hands. The heart of safe use is this: define with a clear policy which data may be entered, do not write personal or trade secret information into public bots, prefer corporate (with a data processing agreement) or local models, and train your staff. Opening a safe path instead of banning also reduces shadow use.

A marketing specialist pastes quarterly figures while preparing a deck, a developer pastes private source code to fix a bug, a lawyer pastes a draft contract into a chatbot. Each wants to speed up their work; none is leaking data on purpose. But together they create an outbound flow of data the organization never sees. This is called shadow AI, and in many organizations today it is the quietest data leak channel.

What exactly is the problem

Everything you type into a public AI assistant is processed on the provider's infrastructure. This carries three separate risks. First, the data is stored on the provider's servers for a while and can be exposed if a breach occurs. Second, in some services the text you enter can be used to improve the model. Third, if personal data is involved, transferring it to a provider abroad creates a separate obligation under data protection law.

The size of the risk depends on what you write. Summarizing a generic text is low risk; pasting customer personal data, a medical record, source code or a contract is high risk. Leaving that distinction to employees' intuition is exactly where the leak begins.

Bans do not work, because

Many organizations' first reflex is to ban AI tools entirely. In practice this rarely works: employees keep using the tool from personal accounts and phones, and the organization can no longer see what is happening. A ban does not remove shadow use, it just makes it invisible.

The more effective approach is to open a safe path: an approved corporate tool, a clear usage policy and a short awareness training. When people have a safe and easy option, they do not choose the risky one.

Which data goes where

Data type Public bot Corporate / DPA in place Local / offline model
Generic, public text Fine Fine Fine
Internal document, draft Risky Fine Fine
Customer personal data Do not Conditional (law) Preferred
Trade secret, source code Do not Conditional Preferred
Health, finance, legal data Do not Conditional Preferred

The message of the table is simple: as sensitivity rises, move toward an option where the data does not leave the organization's control.

Why local models make a difference

One of the most important developments of recent years is that powerful language models can now run on the organization's own server, without ever reaching the internet. In this approach the data never physically leaves the organization; it is neither stored on a provider's server nor used to train the model. In sectors dense with personal data and trade secrets, this is a decisive advantage for compliance and privacy. We detailed DSET's approach in the local and offline AI, sovereignty and privacy article.

How to build the policy

Start with a short, clear acceptable use policy that defines which data may go into which tool. Tie it to a data classification and labeling effort: data labeled confidential cannot go into public tools. If personal data is involved, evaluate the tool's compliance and any cross border transfer conditions; an information security and ISO 27001 consulting framework organizes these decisions. Finally, train your staff; most leaks come not from malice but from lack of awareness.

The KAOS and DSET approach

DSET helps organizations use AI tools safely: usage policy, data classification and, where needed, models that run inside the organization and do not leak data out. Our local AI engine KAOS is this philosophy itself; it is designed to run on the organization's own infrastructure without reaching the internet. The goal is to not have to give up privacy for productivity.

Frequently asked questions

Does what I type into a chatbot really leave? The text you type into a public assistant is processed on the provider's servers and stored for a while per the terms; in some services it can also be used to improve the model. So the text leaves your device and goes to a third party's infrastructure. The size of the risk depends on what you write; for trade secrets and personal data it is a serious leak.

Is using the corporate (paid) version enough? Corporate versions generally offer more protective terms on retention and model training, which is an improvement. But if personal data is transferred abroad, data protection obligations remain. When the highest privacy is required, local models where the data never leaves the organization are a safer choice.

How do I convince my employees? Not with a ban, but with ease. People use the tool because it speeds up their work; when you offer a safe and practical alternative, they drop the risky path. A clear policy, an approved tool and a short training reduce shadow use far more effectively than bans.

Sources

To build safe AI tool use in your organization or evaluate a local model that does not leak data, contact DSET. We provide consulting from our Ankara Hacettepe Teknokent laboratory.