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Can Your Security Stack See ChatGPT? Why Network Visibility Matters. Generative AI platforms like ChatGPT, Gemini, Copilot, and Claude are increasingly common in organizations. While these solutions improve efficiency across tasks, they also present new data leak prevention for generative AI challenges. Sensitive information may be shared through chat prompts, files uploaded for AI-driven summarization, or browser plugins that bypass familiar security controls. Standard DLP products often fail to register these events. Solutions such as Fidelis Network® Detection and Response (NDR) introduce network-based data loss prevention that brings AI activity under control. This allows teams to monitor, enforce policies, and audit GenAI use as part of a broader data loss prevention strategy. Why Data Loss Prevention Must Evolve for GenAI Data loss prevention for generative AI requires shifting focus from endpoints and siloed channels to visibility across the entire traffic path. Unlike earlier tools that rely on scanning emails or storage shares, NDR technologies like Fidelis identify threats as they traverse the network, analyzing traffic patterns even if the content is encrypted. The critical concern is not just who created the data, but when and how it leaves the organization's control, whether through direct uploads, conversational queries, or integrated AI features in business systems. Monitoring Generative AI Usage Effectively Organizations can use GenAI DLP solutions based on network detection across three complementary approaches: URL-Based Indicators and Real-Time Alerts Administrators can define indicators for specific GenAI platforms, for example, ChatGPT. These rules can be applied to multiple services and tailored to relevant departments or user groups. Monitoring can run across web, email, and other sensors. Process: Advantages: Considerations: Metadata-Only Monitoring for Audit and Low-Noise Environments Not every organization needs immediate alerts for all GenAI activity. Network-based data loss prevention policies often record activity as metadata, creating a searchable audit trail with minimal disruption. Benefits: Limits: In practice, many organizations use this approach as a baseline, adding active monitoring only for higher-risk departments or activities. Detecting and Preventing Risky File Uploads Uploading files to GenAI platforms introduces a higher risk, especially when handling PII, PHI, or proprietary data. Fidelis NDR can monitor such uploads as they happen. Effective AI security and data protection means closely inspecting these movements. Process: Advantages: Considerations: Weighing Your Options: What Works Best Real-Time URL Alerts Metadata-Only Mode File Upload Monitoring Building Comprehensive AI Data Protection A comprehensive GenAI DLP solutions program involves: Organizations should periodically review policy logs and update their system to address new GenAI services, plugins, and emerging AI-driven business uses. Best Practices for Implementation Successful deployment requires: Key Takeaways Modern network-based data loss prevention solutions, as illustrated by Fidelis NDR, help enterprises balance the adoption of generative AI with strong AI security and data protection. By combining alert-based, metadata, and file-upload controls, organizations build a flexible monitoring environment where productivity and compliance coexist. Security teams retain the context and reach needed to handle new AI risks, while users continue to benefit from the value of GenAI technology.