Summary
In this module, you learned how to apply sensitivity labels to classify and protect items using Microsoft Fabric features and Microsoft Purview Information Protection. You explored automatic labeling capabilities, including default labeling, mandatory labeling, and downstream inheritance. You used endorsement levels (Promoted, Certified, and Master data) to signal which items are trustworthy and ready for use. You documented data assets with descriptions, lineage views, and impact analysis to make your data estate discoverable. Finally, you connected these governance practices to AI consumption, understanding how labels create boundaries, endorsement guides agent trust, and documentation provides the context AI needs for accurate responses.
In our scenario, the organization's data estate was growing without clear signals for trust, sensitivity, or documentation. Users and AI agents had no reliable way to distinguish authoritative data from experimental work, and sensitive information risked exposure to unauthorized consumers.
By applying data governance best practices, the data estate now has clear classification, trust signals, and documentation. Both human users and AI agents can find, evaluate, and consume your data with confidence.