7 Steps to Prevent GenAI Data Leakage

Generative AI (GenAI) tools, such as ChatGPT, have rapidly become integral to organizations worldwide, with 65% of companies already utilizing these technologies extensively, according to the McKinsey Global Survey on the State of AI in 2024. Gartner forecasts that this figure will rise to 80% by 2026.

While GenAI platforms significantly boost productivity, they also introduce new cybersecurity risks that organizations must address. This article explores the primary threats posed by GenAI and outlines strategies to prevent data leakage through ChatGPT and similar AI chatbots.

What Happened?

A notable example is the Samsung Conversational AI leak in 2023, where engineers inadvertently shared proprietary semiconductor source code with ChatGPT, leading to a significant data breach. This incident underscores a critical lesson: Never input intellectual property or confidential data into AI chatbots. As companies adopt GenAI tools for productivity, IT administrators must implement strict security controls.

For instance, when Google’s “Help Me Write” feature was enabled across an enterprise, concerns arose about employees unintentionally exposing sensitive data. A key solution was enrolling Chrome browsers in Chrome Enterprise Core, which allows administrators to enforce Data Loss Prevention (DLP) policies, monitor AI interactions, and mitigate exfiltration risks — striking a balance between security and innovation. Here we’ll break down seven essential steps to prevent GenAI-related data leaks in your organization.

Threats Posed by Generative AI

Generative AI platforms enable users to swiftly analyze large datasets, troubleshoot software issues, schedule meetings, generate reports, create relevant content, and perform various routine tasks. However, the use of these tools presents several cybersecurity threats.

Loss of Intellectual Property

Sharing proprietary information or trade secrets with GenAI platforms can result in a loss of competitive advantage, reduced market presence, and revenue loss. Rivals may gain access to valuable assets and exploit your technologies to their benefit.

Operational Disruption

Data leakage can lead to significant operational disruptions, affecting the continuity and efficiency of business processes.

Compliance Violations

Failure to handle data in compliance with regulations such as GDPR, HIPAA, or CCPA can lead to legal penalties and damages. According to a study by Veritas Technologies, over 52% of businesses acknowledge that most of their employees inadvertently share data in ways that could breach privacy policies, leading to fines and reputational damage.

Unauthorized Access

Data processed by ChatGPT could, in some configurations, become accessible to other users if there's an internal data leak or external breach.

Insider Threats

Employees or contractors with access to GenAI tools may intentionally or unintentionally misuse them, leading to data leaks or unauthorized disclosures.

How to Prevent Data Leakage

To mitigate these risks, organizations should implement a Generative AI policy.

1. Develop a Clear AI Usage Policy

Organizations should establish comprehensive guidelines to clearly define the acceptable and unacceptable uses of Generative AI (GenAI) tools, ensuring employees understand the boundaries of AI tool interactions. These guidelines should outline what types of data can be shared with AI platforms—such as non-sensitive, anonymized information—and specify what data is off-limits, such as confidential, personally identifiable, or proprietary data.

The policy should also set clear consequences for violations, including disciplinary actions or restricted access to AI tools, to deter misuse. By providing clear instructions and enforcing these guidelines, organizations can help protect sensitive information, ensure compliance with data protection laws, and maintain the responsible use of GenAI technology.

2. Implement Data Loss Prevention (DLP) Solutions

DLP solutions help mitigate Generative AI data leaks by blocking access to unauthorized AI tools, monitoring user behavior, and enforcing AI-specific policies. Advanced DLP systems, like Microsoft Purview can detect and prevent employees from submitting sensitive data into AI prompts through keyword detection (alerts when sensitive data (e.g., financials, customer PII, source code) is entered into AI chat windows), clipboard monitoring (preventing direct copy-pasting from corporate applications into AI chat interfaces), and structured data analysis (detecting and blocking structured data formats such as credit card numbers, SSNs, patient records).

Additionally, organizations can use secure enterprise AI solutions, such as Microsoft Copilot (Paid Version) or private AI sandboxes, to ensure that AI-generated outputs remain internal. However, while they can help reduce accidental leaks by warning employees, blocking risky behavior, and limiting GenAI access, DLP solutions cannot fully prevent intentional leaks yet (e.g., an employee manually typing out confidential information). Best practice is to combine DLP with strict security awareness training, insider threat monitoring, and secure enterprise-ready AI solutions.

3. Anonymize Sensitive Data

To protect privacy and minimize the risk of data leaks, organizations should ensure that any identifying information is removed or anonymized before inputting data into Generative AI (GenAI) platforms.

This includes stripping out names, addresses, phone numbers, email addresses, and other personally identifiable information (PII) from datasets, ensuring that sensitive data is not inadvertently exposed.

This way, organizations can safeguard individual privacy, comply with data protection regulations, and reduce the risk of accidental or intentional misuse of sensitive information while still leveraging the benefits of AI technology.

4. Educate and Train Employees

To effectively manage the risks associated with Generative AI (GenAI) tools, organizations must prioritize continuous training for all employees who interact with these platforms. Regular training sessions should not only raise awareness of the potential risks — such as data leaks, inadvertent sharing of sensitive information, or misuse of AI-generated outputs — but also equip employees with the knowledge to mitigate those risks. These sessions should cover best practices for data handling, emphasizing the importance of anonymizing or removing personally identifiable information (PII) before it is input into AI tools. Additionally, staff should be educated on the specific AI usage policies that govern what data can and cannot be shared, ensuring they understand the boundaries of acceptable AI interactions.

The training should also focus on regulatory compliance, including adherence to standards like GDPR or HIPAA, and reinforce the organization's commitment to maintaining security and privacy. By fostering a culture of awareness and responsibility, employees will be better equipped to make informed decisions, follow protocols, and act in the organization's best interests, thus minimizing the risk of unintended data exposure or non-compliant AI usage. Regular training ensures that employees stay updated on the evolving nature of AI risks and tools, reinforcing security across the organization.

5. Restrict Access to AI Tools

To mitigate the risks associated with Generative AI (GenAI) platforms, organizations should limit access to authorized personnel only, ensuring that employees interact with AI tools based on their job roles. This can be achieved by implementing Role-Based Access (RBA) controls, where permissions are granted according to job functions, and regularly auditing access rights.

Additionally, enforcing strong password policies and using Single Sign-On (SSO) enhances security by requiring complex, regularly updated passwords and centralizing user authentication. These measures help reduce unauthorized access, improve compliance with privacy regulations, and strengthen the organization’s overall security posture while enabling safe and productive use of GenAI tools.

6. Monitor and Audit AI Interactions

To effectively monitor and audit AI interactions, organizations should integrate AI activity logging into their SIEM (Security Information and Event Management) systems to detect anomalies, unauthorized data access, and potential leaks.

For example, if an employee frequently queries an AI assistant like Microsoft 365 Co-Pilot for financial projections, intellectual property details, or HR records, the system should flag this behavior for review. Implementing automated alerts based on query patterns — such as repeated requests for sensitive data — can help security teams identify and investigate risks early.

Additionally, organizations should conduct periodic audits of AI-generated outputs to ensure they do not contain unintended confidential information. By leveraging these monitoring techniques, companies can proactively safeguard their data while still benefiting from AI-powered productivity tools.

7. Only Use Secure Enterprise AI Solutions

Secure enterprise AI solutions refer to AI tools that organizations can control and trust because they keep data within the organization's secure environment rather than sending it to public AI models. These solutions are designed to prevent data leaks while still allowing employees to benefit from AI automation.

Secure enterprise AI solutions, such as Microsoft Copilot for Microsoft 365 (Paid Version), Azure OpenAI (Private GPT), Google Vertex AI with Gemini, and self-hosted AI models, allow organizations to run AI tools within their secure environment, ensuring that data stays internal and is not shared or used to train external models. These solutions help organizations maintain control over their data, comply with regulations like GDPR and HIPAA, and apply custom security measures to prevent data leaks, making them ideal for organizations seeking to protect sensitive information while leveraging AI capabilities.

Unlike free AI tools (ChatGPT, Gemini, etc.), these solutions ensure no data is shared externally. They help organizations comply with GDPR, HIPAA, and other regulations. Companies can define rules to control how AI accesses and processes data.

While GenAI tools like ChatGPT offer substantial benefits in terms of productivity and efficiency, they also pose significant cybersecurity risks, including data leakage, loss of intellectual property, and compliance violations. By implementing the practices outlined above, organizations can harness the advantages of GenAI platforms while safeguarding their sensitive information and maintaining regulatory compliance. If you have any questions about GenAI, contact the team at ISEC7 Government Services. We specialize in leveraging solutions to enhance efficiency, security, and service delivery in the government sector and would love to help your organization achieve its goals.