Data Loss Prevention Strategies for M365 Copilot

Overview

Data Loss Prevention is critical when deploying AI-powered productivity tools like M365 Copilot. This video explores how to design and implement DLP policies that protect your agency’s sensitive information without creating friction that discourages productive Copilot use.

Intended for security architects, compliance officers, and IT administrators responsible for information protection in government environments.

What You’ll Learn

• DLP Fundamentals for AI: How Copilot interactions differ from traditional content creation
• Policy Design: Crafting DLP rules that catch genuine risks without false positives
• Sensitivity Labels: Automatic and manual classification strategies for AI outputs
• Testing & Validation: Ensuring DLP policies work as intended with Copilot
• User Experience: Balancing security with productivity

Transcript

[00:00 - Introduction]

Hi everyone, Sarah Johnson here. Today we’re tackling a critical security topic: Data Loss Prevention for Microsoft 365 Copilot. AI introduces new data handling patterns that require thoughtful DLP policy design. Get it right, and you protect sensitive information while enabling productivity. Get it wrong, and you either expose data or frustrate users into abandoning Copilot altogether.

[00:45 - Why Copilot Requires Special DLP Consideration]

Traditional DLP policies were designed for scenarios where users explicitly create and share content—sending an email, uploading a document, or sharing a file. Copilot introduces a different pattern: users provide prompts containing context, and Copilot generates responses that might include sensitive information pulled from your environment.

The challenge is preventing Copilot from inadvertently revealing sensitive data in its suggestions while still allowing it to be useful.

[02:15 - Understanding the Risk Scenarios]

Let’s identify specific risk scenarios where DLP needs to intervene:

Scenario one: A user prompts Copilot “Summarize the personnel records for the contracting team.” If those records contain PII or sensitive performance information, Copilot might surface that data in the summary. DLP should detect sensitive information types in Copilot’s response and block or warn appropriately.

Scenario two: A user drafts an email using Copilot and the AI suggests including a document excerpt that contains CUI or export-controlled information. DLP needs to evaluate the AI-generated content before the email is sent.

Scenario three: In Teams chat, a user asks Copilot about project details, and Copilot references a confidential document. The chat response itself becomes a potential data loss vector.

[04:30 - Policy Design Principles]

When designing DLP policies for Copilot, follow these principles:

One: Focus on outcome, not process. Don’t try to block prompts—users need freedom to ask questions. Instead, evaluate the AI-generated responses for sensitive content before they’re used.

Two: Use information types that match your data classification. If you protect PII, ensure your DLP policies recognize SSNs, dates of birth, and employee IDs. If you protect CUI, ensure CUI markings trigger appropriate actions.

Three: Implement layered controls. Combine DLP with sensitivity labels, so even if DLP has gaps, labeled content provides a second layer of protection.

Four: Educate rather than block when possible. User education prompts are often more effective than hard blocks for borderline cases.

[07:00 - Configuring DLP for Copilot]

In Microsoft Purview, you’ll create DLP policies that apply to specific locations, including:

Exchange Online: Protect AI-generated email content. SharePoint and OneDrive: Protect documents created or modified with Copilot assistance. Microsoft Teams: Protect chat and meeting content involving Copilot. Endpoints: Protect content when users copy AI outputs to local files.

For each location, define conditions based on sensitive information types, sensitivity labels, or content patterns. Configure actions: block, warn, or audit.

[08:30 - Testing Your Policies]

Before enforcing DLP policies, test extensively:

Enable policies in audit mode first. Monitor DLP events for false positives and false negatives. Test with realistic scenarios: have users interact with Copilot using typical work prompts. Verify that legitimate use cases aren’t blocked while genuine risks are caught. Refine information types and conditions based on testing results.

Only move to enforcement after you’ve validated accuracy and user impact.

[09:45 - Balancing Security and Usability]

The worst outcome is users perceiving DLP as an obstacle to Copilot productivity. Avoid this by:

Providing clear, actionable messages when DLP triggers. Educating users on why certain content triggered a policy. Offering alternatives—for example, if sensitive content is detected in a draft email, suggest using secure sharing instead of blocking outright. Regularly reviewing DLP logs to identify pain points and refine policies.

[10:45 - Monitoring and Continuous Improvement]

DLP for AI isn’t a one-time configuration. Plan for continuous improvement:

Monthly review of DLP events and trends. Quarterly policy tuning based on new use cases and data types. Feedback loops with users to understand where policies help and where they hinder. Integration with broader information protection and governance programs.

[11:30 - Conclusion]

Data Loss Prevention for Copilot requires thoughtful design that balances protection with productivity. By focusing on outcomes, testing thoroughly, and continuously refining policies, you can protect your agency’s sensitive information while enabling your workforce to benefit from AI. Download our DLP Policy Templates linked below to jumpstart your implementation.