Data Loss Prevention with Microsoft Purview

DLP addresses the protection goal of confidentiality: it detects and prevents information worth protecting from leaving the organization uncontrolled via email, cloud services, or removable media. Before any tool comes the groundwork: a classification model — four protection classes from "public" to "strictly confidential" have proven effective —, the identification of the "crown jewels", and a working permissions and role concept, because DLP presupposes IAM/PAM that is actually lived in practice. Business ownership of data classification sits with the business units, not with IT.

Purview DLP policies apply where data resides and flows: Exchange Online, SharePoint, OneDrive, Teams chats and channels, endpoints (Windows 10/11 and macOS), Microsoft Defender for Cloud Apps, on-premises repositories via the Information Protection Scanner, as well as Fabric/Power BI workspaces and Microsoft 365 Copilot (preview). Endpoint DLP monitors and controls actions such as copying to USB storage devices or network shares, printing, Clipboard use, browser uploads, and RDP transfers — depending on the rule as audit, warning, or block. On the licensing side, DLP for Exchange, SharePoint, and OneDrive is included starting with Microsoft 365 E3; Teams DLP and Endpoint DLP require E5-class plans.

A proven approach is an escalation logic per protection class — label, warn, encrypt/block — instead of an immediate hard block. Purview supports this with Simulation Mode (policies run for up to 15 days without enforcement against real data), Policy Tips, and block-with-override including a documented Business Justification. For detection, the rule of thumb is: Sensitive Information Types for pattern-based matches, Exact Data Match for matching against your own data sets (only salted hashes leave the organization, resulting in significantly fewer false positives), and Trainable Classifiers for ML-based content detection.

DLP events do not belong in a silo, but in the existing ITSM and incident landscape: detected incidents feed into the regular information security incident process, and the SOC owns DLP incident management. Rule changes run through a defined change process with risk assessment, the four-eyes principle, and a post-implementation review — the broader the scope of an exception, the higher the approval authority, up to the Change Advisory Board including the CISO. Documented exception management through the ticketing system keeps whitelists traceable and auditable.

With Microsoft Purview DSPM for AI, AI apps can be secured centrally: insights into AI usage, one-click policies against data leakage in prompts, and weekly Data Risk Assessments. Microsoft 365 Copilot is addressable as its own DLP location (preview) — Sensitivity Labels can prevent Copilot from processing labeled content; for unmanaged AI apps such as ChatGPT, DLP (preview) applies inline via Edge for Business or at the network level. In addition, VamiSec offers VamiGuard, its own solution for DLP in GenAI usage — classic DLP and GenAI DLP complement each other.

In practice, a concept phase of around three months has proven effective (classification, strategy, policy framework), followed by the implementation phase. The document model has two tiers: a DLP policy adopted by the executive board plus a "living" implementation strategy that the information security officer continuously adapts to technology and infrastructure; a one-pager handles employee communication. For the rollout, organization comes before technology: the policy takes effect first, technical blocks follow with a time offset — accompanied by training and awareness.