Preventing sensitive data leakage to AI tools starts with controlling the endpoint itself. When an employee pastes a report, source code, or customer details into ChatGPT, the information leaves the organization in a second with no way back. An endpoint-based DLP solution identifies the sensitive content even before it is sent, and blocks or logs the action according to a pre-defined policy. This way, the organization continues to enjoy the benefits of AI tools without losing control of its assets.
Why has the leakage of sensitive data to ChatGPT become a major risk?
AI tools have become part of the workday, and with them, a new exit channel for organizational data has opened. Employees upload financial reports, legal documents, source code, and customer lists to get quick answers, without realizing that the information is slipping out of the organization's control. According to a 2026 Netwrix survey, only about 30% of organizations are capable of fully and immediately preventing sensitive data from reaching external AI tools, and this gap is precisely the exposure that attackers and regulators are looking for.
Furthermore, the risk is not solely malicious. Human error by an innocent employee, who is simply trying to summarize a document or debug code, can expose classified information just as much as intentional exfiltration. More importantly, Amendment 13 to the Privacy Protection Law has made the protection of personal data a mandatory legal requirement, meaning that a leak of sensitive information can result in both administrative fines and reputational damage.
What is Shadow AI and how does it expose the organization?
Shadow AI is the use of artificial intelligence tools without the authorization, knowledge, or supervision of the IT department. An employee who installs an AI browser extension or signs up for some free service creates a leakage point that no one is monitoring. The multitude of available tools makes this phenomenon particularly widespread, making it difficult for the organization to even know where its data is located.
The exit channels through which sensitive information leaks to AI tools are diverse, so protection that covers only one of them is not enough:
- Browser: Paste text or upload a file to ChatGPT, Gemini, Claude, or Perplexity
- Local app: An AI tool installed on the computer and operating even without a connection to the corporate network
- Microsoft Copilot: Access to sensitive data through the Microsoft suite that the entire organization is already using
- copy paste Copying code, API secrets, or personal details directly into the model
How does an endpoint-based DLP solution block data leaks to AI tools?
An endpoint-based DLP solution enforces the policy at the computer level itself, so the same protection works whether the employee browses to ChatGPT in the browser, runs a local AI application, or pastes text into an external tool. The system scans the content and context in real time, detects sensitive information such as personal details, financial data, and source code, and applies the appropriate rule even before the information leaves the workstation.
The main advantage is that protection is maintained even when the computer is disconnected from the corporate network, for example when working from home. This enforcement does not depend on a browser extension that can be removed, but rather resides on the endpoint itself. For each event, a different response can be defined, ranging from lenient to strict.
- Documentation only: The data is transmitted, but a full log is recorded for audit and tracking purposes.
- Charity request: The user is asked to explain why they are exporting a sensitive file before the action is approved.
- Block and alert: The action is blocked and the security manager is alerted to the attempt.
What is the difference between a dedicated AI DLP tool and a comprehensive DLP solution?
AI-focused tools protect only a single egress channel. The problem is that if an employee doesn't leak information via ChatGPT, they will do so via email, USB drive, or WhatsApp Web. A comprehensive endpoint-based DLP solution covers all egress channels simultaneously, operating under the philosophy that AI is just another potential data leak channel rather than a separate problem.
ofek dist distributes Netwrix Endpoint Protector in Israel, an endpoint-based DLP solution that supports Windows, macOS, and Linux and enforces a uniform policy even offline. Beyond distribution, ofek dist is an information security company itself, and accompanies customers in Hebrew and Israel time throughout the entire process, from initial survey and mapping to full implementation. This way, the organization gains control over the data going out to AI tools without slowing down ongoing work.
What happens to sensitive information after it enters an AI model?
Once sensitive data is pasted into an external AI tool, the organization loses almost complete control over it. Depending on the service's terms of use, the data may be stored on external servers, processed by third parties, and in some cases even used to train future versions of the model. Customer details, contract terms, or proprietary code that have been leaked in this way cannot be retrieved.
Hence, defense must operate at the source, meaning on the endpoint, rather than attempting to deal with the data after it has already left. This is the fundamental difference between an approach that prevents the leak in advance and one that attempts to respond to it after the fact, when it is already too late.
Response levels of DLP policies against AI tools
In practice, ofek dist accompanies organizations in Israel that use Netwrix Endpoint Protector, a solution developed by Netwrix which serves over 13,000 organizations in 100 countries. The table summarizes the response levels that can be configured in the policy regarding data export to AI tools.
|
Policy response |
What is actually happening |
|
Documentation only |
The information is transmitted and a full log is recorded for audit and tracking purposes. |
|
Charity request |
The user is asked to explain why they are extracting sensitive information prior to approval. |
|
Block and alert |
The action is blocked and the security manager is alerted to the attempt. |
Summary
Preventing sensitive information from leaking to ChatGPT does not require banning employees from using AI tools, but rather restoring the organization’s control over what is shared and where. An endpoint-based DLP solution identifies sensitive information in real time and enforces clear policies across all outbound channels, including AI tools. For consultation and a demo of the solution best suited for your organization, contact the ofek dist team today.
Frequently Asked Questions
Can ChatGPT be enabled while still preventing data leaks?
Yes, that is precisely the purpose of the solution. The policy allows free use of AI tools, but identifies and blocks only the sensitive information defined as prohibited for exfiltration, thereby maintaining productivity.
What types of data can a DLP solution identify?
The solution identifies personal details, financial data, credit card numbers, source code, and API secrets. Organization-specific dictionaries and custom expressions can also be configured to identify unique information.
Does the protection also work when the employee works from home?
Yes, the enforcement sits on the endpoint and therefore operates even when the computer is disconnected from the corporate network. An employee who takes the computer home remains subject to the exact same policy.
Do I need to install a browser extension to block uploads to AI tools?
No, the protection operates at the endpoint level itself and does not depend on a browser extension. Thus, it cannot be bypassed by removing an extension or switching to another browser.
Does a DLP solution also detect unauthorized AI tools that employees install?
Yes, beyond blocking outbound information, the solution provides visibility into the use of AI tools in the organization and makes it possible to identify the use of unauthorized tools. This way, the IT department can regain control over the Shadow AI phenomenon.