The active implementation of artificial intelligence (AI) in corporate processes creates both new opportunities and additional security risks for businesses. The popularity of AI-based chatbots, such as ChatGPT, Gemini, or Copilot, is growing daily, as they assist in text preparation, report generation, data analysis, and code writing. However, along with the benefits, there is a threat of sensitive information leakage: employees may unknowingly share fragments of contracts, client databases, internal documents, and even source code in chats.
This is reported by Finway
Main Threats to Corporate Security from AI Chats
The primary issue remains the human factor: many employees perceive the chatbot as their “personal secretary” and do not consider data protection issues. The lack of clear AI usage policies only exacerbates these risks. When an employee pastes part of a document or table into a chat, the information effectively leaves the corporate perimeter. Additional threats arise from integrations with third-party services, browser telemetry, and caching. The situation is particularly complicated by so-called “shadow AI”: employees use chats from their personal accounts and devices, bypassing IT department control.
These factors create a new threat model that cannot be tracked by standard cybersecurity measures. This is why companies need to implement modern data protection systems.
The Role of DLP Solutions in Protecting Corporate Information
DLP (Data Loss Prevention) is a set of modern technologies that allow for the control of the transfer of confidential information both within and outside the company. Such systems analyze the content of files and messages, detect potentially dangerous fragments, monitor communication channels, and block unauthorized actions.
“If a user attempts to send confidential data outside the corporate environment, the system records the event and responds — blocking the action or notifying specialists. This allows for preventing leaks before they occur.”
DLP solutions need to be integrated into the overall cybersecurity system of the company, combining them with technologies such as SIEM, PAM, and Zero Trust. Important elements also include the use of private LLMs with controlled data storage, regular security policy audits, and staff training. It is this comprehensive approach — a combination of technologies and a culture of responsibility — that ensures reliable protection against data leaks.
GTB DLP: An Effective Tool for Preventing Data Leaks
As an example, we can consider the solutions from GTB Technologies, which combine classic DLP mechanisms with AI usage control. Their systems comply with GDPR and Ukrainian legislation, monitor HTTPS traffic between users and AI applications, recognize sensitive information based on digital fingerprints and classification rules, and track user actions through agents on workstations. This allows for timely detection of potential leaks and adjustment of appropriate responses to real threats to the business.
Particular attention should be paid to the privacy policies of AI services. For instance, the free version of ChatGPT does not store user data, while the paid version may do so with user consent. The Gemini service may use anonymized data to improve its services, but also recommends not sharing confidential information.
In conclusion, the responsibility for data security primarily lies with users and companies, not with developers or owners of AI. A complete ban on the use of AI is not a solution: it is important to implement control systems, train staff, and regularly update protection policies. Companies that have already adopted this approach gain not only reliable protection but also a competitive advantage in the market. The balance between innovation and security is becoming the new standard for corporate resilience.