Microsoft Purview Data Loss Prevention: DLP Across Endpoints, M365, Cloud, and Copilot

An enterprise deploys Purview DLP with 47 policies covering credit cards, SSNs, health records, financial data, and custom sensitive information types. Within two weeks, the DLP incident queue has 8,000 alerts. The security team investigates and finds that 90% are false positives — documents that match patterns but aren't actually sensitive (test credit card numbers in QA systems, formatted numbers that look like SSNs but aren't, financial data that's already public). The team starts ignoring the queue because the signal-to-noise ratio makes investigation impractical. Meanwhile, actual sensitive data continues to leave the organization through the channels DLP was supposed to protect — because the policies that would catch real incidents are buried in false positive noise.

DLP that protects without paralyzing starts with policy design discipline. Start in simulation mode with every policy — measure false positive rates before enforcement. Tune sensitive information types with confidence thresholds that balance detection with precision. Phase enforcement by location (M365 services first, then endpoints, then cloud apps) so the organization adapts progressively. Use policy tips that educate users before blocking — the user who understands why sharing is restricted is more likely to comply than the user who encounters an unexplained block. Implement adaptive protection so DLP is stricter for users whose insider risk signals indicate elevated risk and lighter for consistently compliant users. Monitor and tune continuously because business processes change and yesterday's false positive threshold becomes tomorrow's gap. Done with this discipline, DLP protects. Done as a checkbox deployment, it generates noise nobody acts on.