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Copy file name to clipboardExpand all lines: articles/search/search-document-level-access-overview.md
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@@ -4,7 +4,7 @@ titleSuffix: Azure AI Search
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description: Conceptual overview of document-level permissions in Azure AI Search.
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author: gmndrg
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ms.author: gimondra
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ms.date: 06/06/2025
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ms.date: 07/03/2025
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ms.service: azure-ai-search
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ms.topic: conceptual
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ms.custom:
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| Approach | Description |
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|----------|-------------|
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| Security filters | String comparison. Your application passes in a user or group identity as a string, which populates a filter on a query, excluding any documents that don't match on the string. <br><br>Security filters are a technique for achieving document-level access control. This approach isn't bound to an API so you can use any version or package. |
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| ACLs (preview) | Microsoft Entra ID security principal behind the query token is compared to the permission metadata of documents returned in search results, excluding any documents that don't match on permissions. <br><br>Built-in access control list (ACL) support for principals is in preview, available in REST APIs and prerelease Azure SDK packages that provide the feature. |
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| ACLs / RBAC scopes (preview) | Microsoft Entra ID security principal behind the query token is compared to the permission metadata of documents returned in search results, excluding any documents that don't match on permissions. <br><br>Built-in support for preserving Access Control Lists (ACLs) and Azure Data Lake Storage (ADLS) Gen2 Role-Based Access Control (RBAC) container scopes at the file level for security principals is in preview, available in REST APIs and prerelease Azure SDK packages that provide the feature. |
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## Pattern for security trimming using filters
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For scenarios where native ACL integration isn't viable, we recommend security filters for trimming results based on exclusion criteria. The pattern includes the following components:
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For scenarios where native ACL/RBAC scopes integration isn't viable, we recommend security filters for trimming results based on exclusion criteria. The pattern includes the following components:
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- Create a string field in the index to store strings of user or group identities.
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- Load the index with source documents that include a field containing the identities.
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This approach is useful for systems with custom access models or non-Microsoft security frameworks. For more information this approach, see [Security filters for trimming results in Azure AI Search](search-security-trimming-for-azure-search.md).
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## Pattern for native support for POSIX-like ACL permissions (preview)
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## Pattern for native support for POSIX-like ACL and RBAC scope permissions (preview)
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Native support is based on Microsoft Entra ID user and group access IDs affiliated with documents that you want to index and query. We recommend group access IDs for ease of management. The pattern includes the following components:
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Native support is based on Microsoft Entra ID user and group access IDs affiliated with documents that you want to index and query. ADLS container RBAC scopes preservation at document level is also supported.
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For ACLs, we recommend group access IDs for ease of management. The pattern includes the following components:
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- Start with documents or files that have ACL assignments.
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-[Enable permission filters](/rest/api/searchservice/indexes/create-or-update?view=rest-searchservice-2025-05-01-preview&preserve-view=true#searchindexpermissionfilteroption) in the index.
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-[How to index document-level permissions using push API](search-index-access-control-lists-and-rbac-push-api.md)
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-[How to index document-level permissions using the ADLS Gen2 indexer](search-indexer-access-control-lists-and-role-based-access.md)
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-[How to query using Microsoft Entra token-based permissions](https://aka.ms/azs-query-preserving-permissions)
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-[How to query using Microsoft Entra token-based permissions](https://aka.ms/azs-query-preserving-permissions)
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