Microsoft 365 Certified: Administrator ExpertImplement and manage Microsoft Purview complianceMedium
A company is transitioning from a legacy on-premises file share to SharePoint Online and OneDrive for Business. They have a significant amount of historical data that contains personally identifiable information (PII) which needs to be classified and protected with sensitivity labels as it's migrated. The company wants to automate the application of these labels to existing files during or after migration without user intervention. Which Microsoft Purview feature is best suited for this task?
- AAuto-labeling policies for SharePoint and OneDrive
- BMicrosoft Purview Records Management for file classification
- CData Loss Prevention (DLP) policies with 'block' actions
- DManual application of sensitivity labels by users
Show answer & explanationAnswer & explanation
Correct answer: A. Auto-labeling policies for SharePoint and OneDrive
Auto-labeling policies for SharePoint and OneDrive are specifically designed to automatically apply sensitivity labels to content at rest or when it's uploaded, based on conditions like the presence of sensitive information types (e.g., PII). This allows for automated classification and protection of large volumes of existing data without requiring user intervention, making it ideal for migration scenarios.
Why the other options are wrong
- B. Records Management focuses on retention and deletion, not the automatic classification and protection with sensitivity labels.
- C. DLP policies are for preventing data loss, not for applying sensitivity labels for classification and protection during migration.
- D. Manual application is not suitable for migrating 'significant amount of historical data' and contradicts the 'without user intervention' requirement.
Auto-labeling Policies
Microsoft Purview policies that automatically apply sensitivity labels to content in SharePoint, OneDrive, and Exchange based on predefined conditions like sensitive information types or keywords.
- Automates sensitivity label application.
- Targets content at rest or in transit.
- Uses conditions like SITs, keywords, or trainable classifiers.
- Reduces reliance on manual user classification.
Memory trick: Auto-labeling: Your data gets tagged, no human needed.