Sensitive data auto-classification arrives for SharePoint, OneDrive, and Exchange

Microsoft has announced the general availability of automatic classification with sensitivity labels in SharePoint, OneDrive, and Exchange. Users can create an auto-labelling policy with rules tailored for an organization’s sensitive data, targeting specific locations in your enterprise.

A policy can either be in simulation or active mode. You can run the policy first in simulation mode and if the results satisfy your organization’s needs then you can proceed and publish the policy. 

With our 100+ out-of-the-box sensitive information types and ability to create custom ones, you have the flexibility to tailor the auto-labelling policy to specific sensitive information types. You can also scope the policy to a specific SharePoint site or OneDrive account or Exchange mailbox.

Policy Simulator provides insight into policy effectiveness and enables you to simulate in your production environment with real data with no impact on end users until the policy is published.

Auto classification with sensitivity labels, along with Policy Simulator, is a powerful capability that enable organizations to automatically designate eligible Excel, PowerPoint, Word files, and emails as sensitive in a scalable way.

Your users can search for content within these protected documents, co-author using Office web apps and be assured that the protection will persist even after the documents are downloaded. This way your security needs are in harmony with your user’s productivity needs.

Microsoft 365 users can turn on this feature in Microsoft 365 compliance center. This advanced capability is included with Microsoft 365 SKUs (E5, E5 Compliance and E5 Information Protection & Governance) and Office 365 E5 SKU. You can learn more about licensing here.

Figure 1. Auto label policy across two modes: simulation and active modes

Figure 2. Auto labelling policy simulation mode results

Figure 3. Document library experience in SharePoint showing files automatically labelled


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