How to control employee use of unauthorized AI tools

Controlling employees' use of unauthorized AI tools begins with gaining complete visibility into which tools are actually operating in the organization, and only afterwards enforcing policies on them. Employees are adopting artificial intelligence tools faster than the IT department can approve them, thus creating the Shadow AI phenomenon where sensitive data flows into tools that no one monitors. An endpoint-based DLP solution identifies the unauthorized tools, provides visibility into them, and makes it possible to decide what to block, what to log, and what to allow. This is how the organization transitions from a state of complete ignorance to a state of data-driven control, without stopping daily operations.

What is Shadow AI and why is it difficult to control?

Shadow AI is the use of artificial intelligence tools within an organization without the approval or supervision of the IT department. The main difficulty is that most tools are available for free and at the click of a button, via the browser or as an extension, so any employee can start using them in an instant. Without visibility, the organization simply does not know which tools are active and what information has already been transmitted through them.

The result is a security blind spot. A CISO cannot protect what they cannot see, and therefore the first step in controlling unauthorized AI tools is always mapping and identifying actual usage.

The scale of the phenomenon is larger than it seems. Even in organizations with an official policy, many employees use AI tools outside the approved boundaries, usually out of a genuine desire to get more done. Therefore, dealing with Shadow AI cannot rely on trust alone, but must be based on technological monitoring that reveals the true picture and enables action to be taken accordingly.

How do you achieve visibility for AI tools operating in the organization?

Visibility is achieved through an agent sitting on the endpoint that identifies which applications and websites users access, including AI tools. Instead of relying on employee statements, the organization gets a factual picture of the tools in use, the frequency of use, and the type of data flowing into them. Based on this picture, it is possible to build a policy based on data rather than guesswork.

The types of tools that are important to identify and map are diverse, so the control must cover all of them:

• Cloud models: ChatGPT, Gemini, Claude, Perplexity, and DeepSeek accessible via the browser
• Local apps: AI tools installed on the computer and operating even without an organizational network
• Browser extensions: AI browser extensions that gain access to page content
• Built-in AI assistants: Assistants integrated into existing tools and accessing organizational data

How do you enforce AI tool usage policies without halting work?

Proper enforcement is not a blanket block, but rather a tiered policy that distinguishes between tool and tool, department and department, and types of information. It is possible to approve the use of a certain AI tool and block another, allow free use of general content and block only sensitive information, and tailor the rules to each employee group. In this way, the organization maintains productivity while simultaneously preventing the leakage of sensitive information.

The key is that the enforcement sits on the endpoint and does not depend on an extension that can be removed. An employee trying to transfer sensitive information to an unauthorized tool encounters the policy whether they are on the corporate network or working from home.

It is important to remember that AI usage policy does not remain static. As new tools enter the market and work patterns change, the policy needs to be updated accordingly. A solution that provides continuous visibility enables the organization to identify new tools that have come into use and make decisions about them, rather than discovering them only after an incident has already occurred. Thus, control over Shadow AI becomes a living process rather than a one-time project.

What are the risks of data leakage to an unauthorized AI tool?

The main risk is the loss of control over the data. Once an employee inputs sensitive information into an external AI tool, the data may be stored on servers outside the organization's control, processed by third-party entities, and in some cases even used to train the model. Customer details, financial documents, or proprietary code that leave in this manner are irretrievable.

Furthermore, the use of unapproved AI tools creates regulatory exposure. Amendment 13 to the Privacy Protection Law requires the protection of personal data, and the leakage of such data to an external tool may be considered a violation. Therefore, control over AI tools is not only an operational matter but also part of compliance with legal requirements, an issue that currently concerns both the Chief Information Security Officer and management.

How does ofek dist help control AI tools?

ofek dist distributes Netwrix Endpoint Protector in Israel, an endpoint-based DLP solution that provides visibility into AI tool usage and enforces policies on them in real time across Windows, macOS, and Linux, including offline. The solution identifies unauthorized tools and allows configuring a different response for each, ranging from logging to complete blocking.

Beyond the product itself, ofek dist is an information security company that guides organizations in building their AI usage policy, in Hebrew and according to Israel time, from initial mapping to full implementation. This is how the Shadow AI phenomenon transforms from a blind spot into a managed and controlled domain.

What types of AI tools are important to monitor?

ofek dist distributes Netwrix solutions in Israel, serving over 13,000 organizations across 100 countries. According to a 2026 Netwrix survey, only about 30% of organizations are able to fully prevent data leaks to AI tools. The table summarizes the types of tools that are important to identify and monitor.

Summary

Controlling the use of unapproved AI tools is not a matter of prohibition, but of visibility and smart policy. An endpoint-based DLP solution provides both capabilities, allowing organizations to benefit from AI tools without losing their sensitive data. For a Shadow AI mapping and a solution demonstration, contact the ofek dist team today.

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