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:
- When a user accesses a GenAI endpoint, Fidelis NDR generates an alert
- If a DLP policy is triggered, the platform records a full packet capture for subsequent analysis
- Web and mail sensors can automate actions, such as redirecting user traffic or isolating suspicious messages
Advantages:
- Real-time notifications enable prompt security response
- Supports comprehensive forensic analysis as needed
- Integrates with incident response playbooks and SIEM or SOC tools
Considerations:
- Maintaining up-to-date rules is necessary as AI endpoints and plugins change
- High GenAI usage may require alert tuning to avoid overload
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.
- Alerts are suppressed, and all relevant session metadata is retained
- Sessions log source and destination IP, protocol, ports, device, and timestamps
- Security teams can review all GenAI interactions historically by host, group, or time frame
Benefits:
- Reduces false positives and operational fatigue for SOC teams
- Enables long-term trend analysis and audit or compliance reporting
Limits:
- Important events may go unnoticed if not regularly reviewed
- Session-level forensics and full packet capture are only available if a specific alert escalates
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:
- The system recognizes when files are being uploaded to GenAI endpoints
- DLP policies automatically inspect file contents for sensitive information
- When a rule matches, the full context of the session is captured, even without user login, and device attribution provides accountability
Advantages:
- Detects and interrupts unauthorized data egress events
- Enables post-incident review with full transactional context
Considerations:
- Monitoring works only for uploads visible on managed network paths
- Attribution is at the asset or device level unless user authentication is present
Weighing Your Options: What Works Best
Real-Time URL Alerts
- Pros: Enables rapid intervention and forensic investigation, supports incident triage and automated response
- Cons: May increase noise and workload in high-use environments, needs routine rule maintenance as endpoints evolve
Metadata-Only Mode
- Pros: Low operational overhead, strong for audits and post-event review, keeps security attention focused on true anomalies
- Cons: Not suited for immediate threats, investigation required post-factum
File Upload Monitoring
- Pros: Targets actual data exfiltration events, provides detailed records for compliance and forensics
- Cons: Asset-level mapping only when login is absent, blind to off-network or unmonitored channels
Building Comprehensive AI Data Protection
A comprehensive GenAI DLP solutions program involves:
- Maintaining live lists of GenAI endpoints and updating monitoring rules regularly
- Assigning monitoring mode, alerting, metadata, or both, by risk and business need
- Collaborating with compliance and privacy leaders when defining content rules
- Integrating network detection outputs with SOC automation and asset management systems
- Educating users on policy compliance and visibility of GenAI usage
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:
- Clear platform inventory management and regular policy updates
- Risk-based monitoring approaches tailored to organizational needs
- Integration with existing SOC workflows and compliance frameworks
- User education programs that promote responsible AI usage
- Continuous monitoring and adaptation to evolving AI technologies
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.