Build Sensitivity Label‑Aware, Secure RAG with Azure AI Search and Purview

gia_mondragon

Microsoft

Feb 24, 2026

1. Introduction: Why This Matters Now

Most developers building solutions with Azure AI Search haven’t had to think about Microsoft Purview sensitivity labels before.

Sensitivity labels are applied at the source—SharePoint, OneLake, OneDrive—and they classify and protect documents through encryption, access rules, and usage rights. As a result, developers often don’t see these labels directly, and many are unaware that labeled or encrypted documents behave differently when used in AI and search workloads.

This matters because RAG and Copilot‑style applications rely on complete, context‑rich data to return accurate answers. If labeled content isn’t accessible to the indexing pipeline—or if Azure AI Search isn’t configured to interpret label metadata—your retrieval layer may unintentionally miss protected documents, leading to incomplete grounding, reduced answer quality, or inconsistent user experiences.

For context, Copilot-style apps are context‑aware AI applications that combine a large language model (LLM) with enterprise data to help users ask questions, generate content, and complete tasks inside an existing workflow.

Historically, search experiences haven’t fully honored Purview label protections. While Azure AI Search can enforce document‑level permissions in sources such as SharePoint in Microsoft 365, ADLS Gen2, Azure Blob storage (when configured), ACLs only answer who can see the document, whereas sensitivity labels define how the content must be handled once accessed. Also, enterprise security and compliance teams expect label-based access enforcement, when configured.

If Purview integration is not enabled, documents with certain label protections—especially encrypted ones—may simply not be indexable, which reduces the corpus available to AI Search. This blog explains how Azure AI Search now integrates with Purview sensitivity labels, why this configuration is increasingly important for secure and complete enterprise RAG, and how to enable it in your environment.

2. What Are Sensitivity Labels & Why They Impact AI Search

Microsoft Purview sensitivity labels classify and protect organizational data by applying encryption, access controls, and visual markings across documents, emails, and collaboration spaces.

Image 1. Assigning sensitivity label to a document

When labels are applied, Microsoft Purview governs among other functionality:

Who can read a document
Whether it’s encrypted
What usage rights apply
How the data must be treated

Purview sensitivity labels and Azure AI Search

Developers often assume these label-based enforcements “just work,” but unless Azure AI Search is configured to extract and evaluate label metadata, AI systems cannot retrieve protected content and/or enforce the behavior expected of data carrying those labels, leading to incomplete and sometimes, insecure, RAG answers.

Azure AI Search now supports the following actions as part of sensitivity label support in preview:

3. What the Integration Enables

(And What Happens If You Don’t Turn It On)

When purview labels are integrated with AI Search

Labeled documents are successfully indexed
Label metadata is stored alongside the document
Query-time filters enforce Purview EXTRACT rights
RAG apps, copilots, and agents return only what a user can access
No risk of “silent missing labeled-context” in retrieval
Unified Purview governance across Microsoft 365 documents and AI Search

Image 2. Example of application showing different documents with sensitivity labels assigned and respecting permissions and encryption (if applicable)

If You Don’t Enable It

Documents with labels with configured protections won’t index, leading to incomplete data available for AI Search, reducing answer quality
Search results won’t enforce protections based on labels, impacting user experience
End users won't have visibility into labels applied to their documents based on compliance requirements, impacting user experience as well