Revised updates per Gia POSIX statement, added link to Matt's query ACLs doc

Author: HeidiSteen

articles/search/search-document-level-access-overview.md

@@ -23,21 +23,23 @@ Azure AI Search provides two approaches for document-level access control: nativ
 
 For scenarios where native ACL and RBAC integration isn't supported, Azure AI Search enables [security trimming using query filters](search-security-trimming-for-azure-search.md). By creating a field in the index to represent user or group identities, you can use the filters to include or exclude documents from query results based on those identities. This approach is useful for systems with custom access models or non-Microsoft Entra-based security frameworks.
 
-### Native support for ADLS Gen2 permissions (preview)
+### Native support for POSIX-like ACL permissions (preview)
 
-Through Microsoft Entra ID, the [ADLS Gen2 access control model](/azure/storage/blobs/data-lake-storage-access-control-model) supports both Azure role-based access control (Azure RBAC) and POSIX-like access control lists (ACLs). In Azure AI Search using the newest preview APIs, you can flow these permission through to a search index and queries.
+Through Microsoft Entra ID, the [ADLS Gen2 access control model](/azure/storage/blobs/data-lake-storage-access-control-model) supports both Azure role-based access control (Azure RBAC) and POSIX-like access control lists (ACLs). In Azure AI Search using the newest preview APIs, you can flow these permission through to a search index and queries. 
+
+ADLS Gen2 provides ACLs in a format that works well for this approach, but you can use any data source that provides permission data in the same format.
 
 #### Retrieve permissions metadata during data ingestion process
 
 Azure AI Search enables you to push document permissions directly into the search index alongside the content, enabling consistent application of access rules at query time. This capability is achieved in two ways:  
 
-- Use the [REST API](/rest/api/searchservice/operation-groups) or supported SDKs to [push documents and their associated permission metadata](search-index-access-control-lists-and-rbac-push-api.md) into the search index. This approach is ideal for systems with [Microsoft Entra](/Entra/fundamentals/what-is-Entra)-based [Access Control Lists (ACLs)](/azure/storage/blobs/data-lake-storage-access-control) and [Role-based access control (RBAC) roles](/azure/role-based-access-control/overview), such as [Azure Data Lake Storage (ADLS) Gen2](/azure/storage/blobs/data-lake-storage-introduction). By embedding ACLs and RBAC container metadata within the index, developers can reduce the need for custom security trimming logic during query execution.      
+- Use the [REST API](/rest/api/searchservice/operation-groups) or supported SDKs to [push documents and their associated permission metadata](search-index-access-control-lists-and-rbac-push-api.md) into the search index. This approach is ideal for systems with [Microsoft Entra](/Entra/fundamentals/what-is-Entra)-based [Access Control Lists (ACLs)](/azure/storage/blobs/data-lake-storage-access-control) and [Role-based access control (RBAC) roles](/azure/role-based-access-control/overview), such as [Azure Data Lake Storage (ADLS) Gen2](/azure/storage/blobs/data-lake-storage-introduction). By embedding ACLs and RBAC container metadata within the index, developers can reduce the need for custom security trimming logic during query execution.
 
 - For [built-in ADLS Gen2 indexers](search-indexer-access-control-lists-and-role-based-access.md), you can use the preview REST API with the permission filter options to flow existing ACLs and RBAC permissions to your search index. This indexer pulls ACLs and RBAC roles at container level during the data ingestion process, enabling a low/no-code workflow for managing document-level permissions.  
 
 #### Enforce document-level permissions at query time
- 
-With native [token-based querying](https://aka.ms/azs-query-preserving-permissions), Azure AI Search validates a user's [Microsoft Entra token](/Entra/identity/devices/concept-tokens-microsoft-Entra-id) to enforce ACLs and RBAC roles automatically. This functionality helps trim result sets to include only documents the user is authorized to access. You can achieve automatic trimming by attaching the user's Microsoft Entra token to your query request.
+
+With native [token-based querying](https://aka.ms/azs-query-preserving-permissions), Azure AI Search validates a user's [Microsoft Entra token](/Entra/identity/devices/concept-tokens-microsoft-Entra-id) to enforce ACLs and RBAC roles automatically. This functionality helps trim result sets to include only documents the user is authorized to access. You can achieve automatic trimming by attaching the user's Microsoft Entra token to your query request. For more information, see [Query-Time ACL and RBAC enforcement in Azure AI Search](search-query-access-control-rbac-enforcement.md).
 
 ## Benefits of document-level access control