Go beyond one-time cleanups, build sustainable governance with Purview sensitivity labels and DLP to secure Copilot and ensure compliance.
In my previous post, I introduced two tracks for preparing Microsoft 365 data for Copilot:
- One-time cleanup – Identify and remediate overshared files using SharePoint reports, Purview content searches, and DSPM for AI risk insights.
- Ongoing protection – Implement sustainable controls with Microsoft Purview to prevent future overexposure.
While track 1 is crucial to a successful start with Copilot, today we will focus on the second track: building ongoing, AI-aware governance controls that scale and sustain ongoing protection from oversharing, and help with overall data security and compliance efforts.
Why Ongoing Data Protection for Copilot Matters
While critical, the one-time cleanup is a bandage– it fixes today’s problem but doesn’t provide an ongoing cure that prevents tomorrow’s. New email, file, and data creation never stops, and oversharing risks return quickly without systemic controls. Instead of repeatedly chasing exposures, invest in foundational practices that:
- Scale with your growing data stores
- Align with AI tools like Copilot
- Reduce manual effort over time.
You don’t need a full governance program to start, and it is important to remember that Copilot will never show someone data that they cannot access otherwise. Basic Purview features can deliver immediate value and set the stage for long-term maturity.
The Keystone Functions of Ongoing Protection
Microsoft Purview Sensitivity Labels for Copilot Governance
- Label-based encryption can be used to control access for internal and external users.
- Copilot indexes all data in the tenant by default, but will not show a labeled file to someone unless the label permits it.
- You can create purpose-built labels that specifically block the Copilot service from indexing specific files. (This also requires a corresponding DLP policy, more on that in the next section.)
- Labels can always be applied or changed manually. E5 license holders can also take advantage of Purview’s automation features. Labels can be applied automatically based on:
- The detected sensitive content of either new or existing files or messages (PII, financial data),
- At the time of creation of a new message or document,
- Or by default based on the data’s storage location (mailbox, library, or folder).
Data Loss Prevention (DLP) Policies for Copilot Security
- DLP policies can be configured to restrict internal staff from accessing detected sensitive data types based on identity, so that unlabeled (or mislabeled) data cannot be accessed via Copilot.
- Copilot can be blocked from indexing specifically labeled files with a DLP policy.
- Endpoint DLP policies can monitor and/or block sensitive data from being entered into Copilot or other AI tools.
- Similarly, endpoint DLP can prevent shadow AI tools from accessing sensitive data while allowing sanctioned tools like Copilot or ChatGPT Enterprise.
- Purview’s Data Security Posture Management (DSPM) for AI provides a single pane of glass to monitor and report on DLP policies specific to AI usage and management.
Beyond Copilot: Purview and the Bigger Data Security Picture
Sensitivity labels and DLP aren’t just about AI, they’re required to meet and maintain data security and compliance requirements as well. Most common frameworks like NIST, ISO, PCI-DSS, and HIPAA require data classification and data leakage protection by also providing the following benefits:
- Protect PII and other regulated data from internal or external misuse.
- Reduce risk of data exfiltration from compromised accounts.
- Simplify audits, penetration testing, and compliance attestations.
Put another way, a secure environment for Copilot or other AI tools also results from having good governance in place overall.
Layer, Don’t Choose
If you have Microsoft 365 E3, E5, or Purview add-ons, Sensitivity Labels and DLP are the best long-term play and provide ongoing protection. Combining them with an initial data and oversharing cleanup effort will put you in the best position to remain secure, compliant, and help everyone sleep a little better from now on.
The following chart can help you compare and contrast the two tracks:
One-Time Cleanup vs. Ongoing Governance: What’s the Difference?
| Criteria | Ongoing (Purview Labels + DLP) | One-Time Fix (Access Reviews + SAM) |
| AI-Aware Protection | ✅ Integrated with AI controls | ❌ No context for AI risk |
| Granularity | ✅ File- and content-level | ❌ Site- or library-level |
| Scalability | ✅ Automates classification | ❌ Manual effort scales poorly |
| Auditability | ✅ Logs and telemetry from labels | ❌ Limited audit trail |
| Ease of Implementation | ❌ Requires setup and training | ✅ Simple to deploy quickly |
| License Requirement | ❌ M365 E5 or compliance add-on | ✅ Works with lower-tier licenses |
| Time to Value | ⏳ Medium-term payoff | ⚡ Immediate but short-lived fix |
| Compliance Readiness | ✅ Supports ongoing compliance | ❌ Point-in-time reviews only |
Bottom Line
This isn’t about choosing one approach over the other; it’s about maturity. Start where you are, automate what you can, and layer your defenses. Done right, data protection becomes invisible—and AI readiness becomes inevitable.
Ready for Smarter AI Governance?
Secure your data, strengthen compliance, and unlock the full potential of Microsoft Copilot with eGroup.
