Guide
What is shadow AI?
Shadow AI is employees using AI tools for work that their company has not approved, cannot see or cannot oversee. A customer list pasted into a personal ChatGPT account, a contract uploaded to a free translation tool or a coding assistant installed without permission are typical examples.
Why does shadow AI happen?
Because AI genuinely works. Employees find tools on their own that speed up writing reports, debugging code and drafting emails. If the company has no official option, or the official option is slow and restricted, people use the nearest tool in their browser. Shadow AI usually comes not from bad intent but from wanting to get work done.
Risks
- Data leakage: Customer information, financial statements, source code and strategy documents go to a service outside the company. The company cannot know where the data is stored or how it is used.
- Data protection and transfers abroad: Sending text that contains personal data to an AI service in another country can count as processing and transferring personal data under laws such as the GDPR or Turkey’s KVKK (Law No. 6698). The company, as data controller, remains responsible even if it never knew about it.
- Loss of visibility: When no one knows which team uses which tool with which data, risk cannot be measured and incidents cannot be traced.
- Unchecked agents: Agents connected to code repositories, email or cloud accounts can operate with broad permissions without anyone noticing.
Why banning it does not work
Blocking AI tools entirely looks safe in the short term, but it pushes usage onto personal devices and accounts. The company ends up giving up productivity and losing even more visibility. The more sustainable path is to offer AI through a secure channel and to oversee that channel.
How to bring shadow AI under control
- Take inventory: Find out which tools are used, by which teams, with which data.
- Classify your data: Decide which information must never leave and which may be shared in a limited way.
- Offer an approved channel: Give employees a secure way to reach the models they need; otherwise shadow use continues.
- Limit context: Use a layer (an AI gateway) that sends the model the minimum information a task needs instead of the whole document.
- Narrow agent permissions: Make sure agents can perform only the actions their task requires and that risky actions wait for human approval.
- Write the policy and explain it: Prepare a short, clear usage policy that explains why the rules exist.
Derbent AI’s approach
Instead of banning AI, Derbent AI places a controlled boundary between your data and the models: documents stay inside, only narrow and relevant questions reach the models, and agent actions are checked against the scope of the task. The platform is not yet publicly available; you can see the approach step by step on the homepage.