Rethinking Data Loss Prevention for Generative AI

As the integration of generative AI platforms like ChatGPT, Gemini, Copilot, and Claude becomes more prevalent in organizations, the need for robust data loss prevention (DLP) strategies is increasingly evident. While these technologies enhance productivity and streamline operations, they also introduce risks of unintentional data leaks. Sensitive information can be inadvertently shared through chat prompts, file uploads for AI summarization, or various browser plugins that may bypass established security protocols. Traditional DLP methods often fall short in addressing these emerging risks.

The Necessity of Evolving Data Loss Prevention

To effectively safeguard sensitive data in the age of generative AI, organizations must adapt their DLP strategies. The focus needs to shift from merely protecting endpoints and isolated channels to providing comprehensive visibility throughout the entire traffic pathway. Unlike older DLP tools that primarily scan emails or storage locations, modern network detection solutions, such as Fidelis Network Detection and Response (NDR), track data as it traverses the network, even if the content is encrypted.

Organizations must concern themselves not only with the origin of data but also with its flow—how and when it leaves their control. This encompasses direct uploads, conversational queries within AI tools, and integrated AI functionalities embedded in various business systems.

Monitoring Generative AI Use: A Multifaceted Approach

To effectively monitor the use of generative AI, organizations can leverage several complementary methods focused on network detection:

URL-Based Indicators and Real-Time Alerts

Administrators can set specific indicators for distinct generative AI platforms, such as ChatGPT. These tailored rules can be applied across multiple services, making adjustments based on the needs of different departments or user groups.

Process:

• Whenever a user accesses a specific generative AI endpoint, Fidelis NDR triggers an alert.
• If a DLP policy is activated, the system conducts a full packet capture for later analysis.
• Web and email sensors can automatically react to suspicious activities, perhaps by redirecting user traffic or isolating potentially harmful messages.

Advantages:

• Real-time alerts enable immediate responses to security incidents.
• Supports thorough forensic analysis when needed.
• Can integrate seamlessly with existing incident response protocols and security information and event management (SIEM) systems.

Considerations:

• Rules must be kept current, as generative AI endpoints and plugins frequently evolve.
• High levels of AI usage may necessitate fine-tuning alerts to avoid overwhelming security teams.

Metadata-Only Monitoring: A Low-Noise Strategy

Not every organization requires immediate alerts for all generative AI activities. Network-based DLP strategies can also log interactions as metadata, creating searchable audit trails with minimal disruption.

• This approach suppresses alerts while retaining relevant session metadata.
• Logs include essential information such as IP addresses, protocols, devices, and timestamps.
• Security teams can review historical interactions related to generative AI by host, group, or specific time frames.

Benefits:

• Reduces false positives, alleviating pressure on Security Operations Center (SOC) teams.
• Facilitates long-term trend analysis and compliance reporting.

Limits:

• Critical events may be overlooked if logs are not regularly reviewed.
• In-depth forensics and packet captures are only available if an alert is escalated.

Many organizations adopt this metadata-only approach as a foundational strategy, enhancing it with active monitoring in higher-risk areas.

Detecting Risky File Uploads

Uploading files to generative AI platforms carries significant risks, particularly when handling personally identifiable information (PII), protected health information (PHI), or proprietary content. Monitoring these uploads as they happen is crucial in preventing unauthorized data exposure.

Process:

• The system identifies when files are uploaded to generative AI endpoints.
• DLP policies automatically scrutinize file contents for sensitive data.
• If a rule is triggered, the complete context of the upload session is recorded, even without user login, ensuring accountability through device attribution.

Advantages:

• Can disrupt unauthorized data transfers during uploads.
• Provides comprehensive insights for post-incident reviews.

Considerations:

• Monitoring is effective only when uploads occur over managed network paths.
• Attribution may be limited to asset identification unless user authentication is involved.

Weighing Your Options: What’s Most Effective?

Real-Time URL Alerts

• Pros: Allows for swift interventions and thorough investigations.
• Cons: Could lead to increased alerts in high-use environments and requires ongoing maintenance of rules.

Metadata-Only Mode

• Pros: Minimal operational noise, ideal for audits and deeper reviews.
• Cons: Not suited for immediate action; necessitates investigations post-event.

File Upload Monitoring

• Pros: Focuses directly on data exfiltration and provides comprehensive records for compliance.
• Cons: May lack visibility into off-network activities and is limited in identifying users during anonymous uploads.

Constructing a Robust AI Data Protection Framework

Establishing an effective generative AI DLP program necessitates several key components:

• Keeping current lists of generative AI endpoints and regularly updating monitoring rules.
• Assigning appropriate monitoring modes based on risk and business requirements.
• Collaborating with compliance teams to create comprehensive content guidelines.
• Integrating network detection results with SOC automation and asset management systems.
• Educating staff on policy adherence and the visibility of generative AI usage.

Organizations should periodically evaluate their policy logs and refresh systems to address new generative AI services and emerging applications.

Best Practices for Effective Implementation

A successful rollout of generative AI DLP measures requires:

• Clear management of platform inventories and ongoing policy adjustments.
• Risk-based monitoring strategies tailored to specific organizational needs.
• Integration with pre-existing SOC processes and compliance protocols.
• Programs that educate users on responsible AI practices.
• Continuous observation and adjustments in response to new generative AI developments.

In this evolving landscape, modern network-based DLP solutions, such as Fidelis NDR, empower enterprises to embrace the benefits of generative AI while simultaneously ensuring robust security against data risks.