How to Let Employees Use AI Without Losing Sensitive Data
More employees are using corporate and public AI assistants such as ChatGPT, Claude, Gemini, DeepSeek and Copilot for everyday work: writing code, drafting emails, summarizing documents and analyzing information. The productivity benefits are clear, but every prompt or file upload can also become a new route for sensitive data to leave the organization.
The practical challenge is not to ban AI by default, but to control what data employees can send to AI services and under what conditions. This article looks at how SearchInform FileAuditor and SearchInform Risk Monitor can work together to support that approach.
AI data leakage is already a real business risk
Recent international data shows that AI-related exposure is no longer theoretical. According to Check Point's July-August 2026 AI Threat Landscape report, around one in every 36 prompts submitted to generative AI tools carried a high risk of sensitive data leakage, and 88% of organizations regularly using these tools recorded at least one high-risk prompt during July 2026.
IBM's 2025 Cost of a Data Breach research found that one in five breached organizations studied experienced a breach linked to shadow AI. Among organizations that reported AI-related security incidents, 97% said they lacked proper AI access controls. IBM also found that a high level of shadow AI added an average USD 670,000 to breach costs.
The issue is therefore not AI itself, but uncontrolled data handling. Employees may paste source code, customer data, financial information, internal documents or credentials into tools that security teams have not approved or cannot monitor.
How to control AI-related data risks
From a security perspective, two things need to be controlled: the sensitive data itself and the channels through which it can be transferred.
SearchInform FileAuditor scans corporate file repositories, identifies documents containing sensitive information and classifies them with labels such as Personal Data, Financial Information or Confidential. These labels can then be used to manage access, control which applications may open a file, monitor file operations and review permissions.
This gives security teams a structured view of what needs protection, where it is stored and who can access it.
At the data-transfer layer, SearchInform Risk Monitor applies DLP controls to channels such as email, messengers, web traffic, cloud services and removable media, including traffic to AI services. If a transfer violates a security policy, the system can block it and notify the security team. FileAuditor labels can also be used by the DLP controls, so sensitive files can be recognized immediately without repeating content analysis.
Together, the two approaches answer two different questions: FileAuditor identifies what must be protected, while Risk Monitor controls how that information can leave the organization.
Three practical AI control scenarios
1. AI access is completely blocked
Some organizations work with data that is too sensitive to be processed by external AI services. In this case, access to selected AI websites can be blocked altogether.
A DLP policy can prevent employees from opening services such as ChatGPT, Claude, Gemini, DeepSeek or other public AI tools from corporate devices. This is the strictest approach, but it is not always the most practical one.
A total ban can push employees toward shadow AI if they believe the tools are necessary to work faster. For many organizations, it is more effective to define clear rules and block only specific actions or sensitive content.
2. AI is allowed, but file uploads are blocked
A common risk appears when employees upload whole business documents to an AI service for summarization, translation or analysis. A long contract, financial report, customer list or source-code file can leave the corporate environment in seconds.
There are two ways to control this scenario. First, DLP rules can block file uploads on selected AI websites while still allowing employees to use the service for ordinary text queries. The list can include general-purpose tools such as ChatGPT, Claude, Gemini, DeepSeek and Copilot, as well as specialized services used by particular teams.
Second, FileAuditor can restrict labeled documents from being opened in browsers. An employee can continue editing a confidential file in an approved office application, but cannot open the same file in a browser to upload it to an AI service.
Both systems can show a policy warning to the employee and record the event for the security team. This turns blocking into a practical reminder of the organization's AI-use rules rather than a silent technical restriction.
3. AI is allowed, but confidential data is not
The most flexible model allows employees to use AI freely until a request contains information the organization has defined as sensitive. For example, a manager may use AI to translate a blank contract template, but the transfer should be stopped once real customer, employee or partner data is added.
DLP policies can inspect outgoing content using keywords, regular expressions, data fingerprints and other detection methods. If a prompt or uploaded file contains protected information, the transfer can be blocked while ordinary requests continue to work.
The same control can use FileAuditor labels. Because the file has already been classified, the DLP system can react immediately when someone tries to send it to an AI service.
The takeaway
AI data protection works best when it covers the full data lifecycle. FileAuditor discovers and classifies sensitive information before it is used. Risk Monitor controls transfer channels and can stop protected data at the moment an employee attempts to send it to an AI service.
This combination gives security teams room to choose between a complete ban, blocking file uploads, or allowing AI while preventing only sensitive content from leaving the organization. Clear employee notifications also reinforce internal rules without unnecessarily disrupting legitimate work.
For a deeper look at AI-related leakage scenarios and DLP controls, see the SearchInform white paper AI as a Data Leak Channel. For a broader overview of modern data protection capabilities, see SearchInform Next-Gen DLP.
Organizations can also test SearchInform FileAuditor and Risk Monitor free for 30 days.
ABOUT SEARCHINFORM
SearchInform is an information security and risk management product vendor as well as an MSS provider. The company's clients are more than 4000 companies in 20+ countries. Today, the team has products and services for comprehensive protection against insider threats at all levels of corporate information systems: FileAuditor (the DCAP class solution); DLP system with extended functionality; Risk Monitor (advanced platform for internal threat mitigation); SIEM system, Information Security outsourcing service.
