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How to Prevent Generative AI Data Leakage
Generative AI is transforming industries worldwide with tools like chatbots, coding assistants, and document analyzers. At the same time, with greater use of AI comes greater risk of AI data leakage and unauthorized sharing. To protect sensitive data in AI technologies, your organization needs a strong security posture and the latest innovations in data loss prevention.
What Is Generative AI?
Generative AI (GenAI) uses artificial intelligence to create new content like text, images, or code, based on patterns it learns from existing data. Systems like large language models (LLMs), such as ChatGPT, learn from vast amounts of training data to generate coherent results.
Organizations around the world are using GenAI tools to boost efficiency, reduce costs, and improve decision-making. From automated customer service to software development, these tools are seeing massive demand, with a remarkable 3,464.6% increase in AI/ML application traffic between 2024 and 2025.
Key Generative AI Data Security Challenges
Unfortunately, along with its massive potential, widespread AI adoption brings risks, especially when tools process private or sensitive data. Here are the most important ones to consider:
- Exposure of sensitive data: Many GenAI tools train on content from user prompts and queries to improve their models. If that content happens to be sensitive data like customer PII, internal code, or financial records, it could be leaked in GenAI output.
- Shadow AI use: Much like the risks of shadow IT, use of unapproved AI tools can create blind spots and expose sensitive data to third parties. If the AI vendor retains the data, even unknowingly, it can cause data breaches. However, outright blocking AI tools can hinder productivity.
- Compliance violations: GenAI models handle data in unpredictable and, often, poorly understood ways. Because of this, sensitive data leaked to GenAI may violate privacy, retention, governance, and other regulations, potentially leading to noncompliance and reputational damage.
Best Practices to Safely Adopt GenAI Tools
A strategic, phased approach can help you proactively detect and mitigate risk while unlocking the power of GenAI tools. Follow these five key steps:
1. Block Shadow AI and ML Domains and Applications Early On
Start with a strict approach by blocking access to all AI platforms. With thousands of AI tools on the market, many have unknown risks. Blocking them initially aligns with a zero trust approach, preventing accidental leakage of sensitive data.
2. Identify and Approve AI Tools Based on Security Standards
Evaluate AI applications using strict criteria for safety, privacy, and reliability. Approved tools should offer data protection while creating real value for your organization. Even popular apps like ChatGPT and Microsoft Copilot need to be carefully vetted before being widely adopted.
3. Host AI Tools in Secure Private Servers
Rather than sharing data with public AI platforms, you can host tools like ChatGPT on your own private infrastructure. This ensures your organization has full control, and the AI vendor cannot use your inputs to train the public model.
4. Control Access with SSO, MFA, and Zero Trust Architecture
Protect generative AI tools with a zero trust architecture, including single sign-on (SSO), multifactor authentication (MFA), TLS/SSL traffic inspection, and microsegmentation. This prevents leaks while ensuring only authorized users can interact with authorized apps.
5. Enforce Data Loss Prevention (DLP) Policies
Effective DLP ensures critical data stays under your control, preventing sensitive data from leaving your production environment.
How Zscaler Can Help
Zscaler Generative AI Security delivers enhanced visibility, control, and protection to enable safe adoption and use of generative AI tools. Secure AI models with:
- Instant data discovery: Map how sensitive data moves inside and outside your organization, and expose risky interactions in real time.
- Shadow AI detection: Quickly find and block use of unauthorized AI tools before they compromise sensitive data.
- Smart isolation: Contain risky AI interactions in an isolated browser with risk-based policies.
- User monitoring: Flag risky user behaviors and identify training needs with continuous monitoring.
- Risk-based access: Enforce customizable AI usage rules to allow approved tools while blocking risky ones.
Built into our unified Data Security platform, Generative AI Security enables you to:
- Control AI data risks: Enable safe and productive use of AI applications without the risk of data loss
- Restrict upload methods: Implement granular controls that allow prompts but prevent bulk uploads.
- Learn AI usage trends: Gain prompt-level insights into how your employees are using AI tools.
- Coach users on safe use: Pair with Zscaler Workflow Automation to coach users on AI risk and best practices.
Ready to get started? Safely spark creativity and productivity with Generative AI Security.
Taking off with Microsoft Copilot? Keep control over your data security and oversharing.
FAQ
Generative AI refers to systems that create new content based on learned patterns from training data. Because these systems often process sensitive data, security gaps can lead to data breaches or exposure of confidential inputs.
Generative AI tools often operate in cloud environments, increasing exposure risks. Employees might input business-critical data that could be used as training data and exposed in outputs, creating vulnerabilities.
Most AI tools lack enterprise-grade security. While some offer basic protections, many rely on shared environments where risks like AI data leakage persist. Organizations must implement robust controls such as private hosting and zero trust security to protect sensitive data.
To minimize risk, organizations should block unauthorized AI tools, approve apps based on strict security criteria, host applications privately, enforce DLP policies, and control access with zero trust security measures. This will enable organizations to safely adopt and use generative AI tools.
Yes, Zscaler offers tools designed to monitor and protect generative AI use across enterprise workflows. Its platform helps detect shadow AI, secure sensitive data, control user interactions, and enforce safe AI practices, giving organizations full control over AI usage while closing leak vectors.
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