reverted more changes

Author: HeidiSteen

articles/search/TOC.yml

@@ -332,7 +332,7 @@
     items:
     - name: Create a vector index
       href: vector-search-how-to-create-index.md
-    - name: Index binary data for vector search (preview)
+    - name: Index binary data for vector search
       href: vector-search-how-to-index-binary-data.md
     - name: Query vectors
       href: vector-search-how-to-query.md
@@ -466,9 +466,9 @@
         href: search-security-get-encryption-keys.md
     - name: Document-level security
       items:
-      - name: Security filter pattern
+      - name: Security filters
         href: search-security-trimming-for-azure-search.md
-      - name: Set up a security filter using Microsoft Entra ID
+      - name: Security filters with Microsoft Entra ID
         href: search-security-trimming-for-azure-search-with-aad.md
   - name: Development
     items:

articles/search/search-howto-index-sharepoint-online.md

@@ -58,7 +58,7 @@ Here are the limitations of this feature:
 
 + Renaming a SharePoint folder doesn't trigger incremental indexing. A renamed folder is treated as new content.
 
-+ SharePoint supports a granular authorization model that determines per-user access at the document level. The indexer doesn't pull these permissions into the index, and Azure AI Search doesn't support document-level authorization. When a document is indexed from SharePoint into a search service, the content is available to anyone who has read access to the index. If you require document-level permissions, you should consider [security filters to trim results](search-security-trimming-for-azure-search-with-aad.md) and automate copying the permissions at a file level to a field in the index.
++ SharePoint supports a granular authorization model that determines per-user access at the document level. The indexer doesn't pull these permissions into the index, and Azure AI Search doesn't support document-level authorization. When a document is indexed from SharePoint into a search service, the content is available to anyone who has read access to the index. If you require document-level permissions, you should consider [security filters to trim results](search-security-trimming-for-azure-search.md) and automate copying the permissions at a file level to a field in the index.
 
 + Indexing user-encrypted files, Information Rights Management (IRM) protected files, ZIP files with passwords or similar encrypted content isn't supported. For encrypted content to be processed, the user with proper permissions to the specific file must remove the encryption so the item can be indexed accordingly when the indexer runs the next scheduled iteration.
 

articles/search/search-security-overview.md

@@ -156,12 +156,7 @@ For multitenancy solutions requiring security boundaries at the index level, it'
 
 User permissions at the document level, also known as *row-level security*, isn't natively supported in Azure AI Search. If you import data from an external system that provides row-level security, such as Azure Cosmos DB, those permissions won't transfer with the data as its being indexed by Azure AI Search.
 
-If you require permissioned access over content in search results, there's a technique for applying filters that include or exclude documents based on user identity. This workaround adds a string field in the data source that represents a group or user identity, which you can make filterable in your index. The following table describes two approaches for trimming search results of unauthorized content.
-
-| Approach | Description |
-|----------|-------------|
-|[Security trimming based on identity filters](search-security-trimming-for-azure-search.md)  | Documents the basic workflow for implementing user identity access control. It covers adding security identifiers to an index, and then explains filtering against that field to trim results of prohibited content. |
-|[Security trimming based on Microsoft Entra identities](search-security-trimming-for-azure-search-with-aad.md)  | This article expands on the previous article, providing steps for retrieving identities from Microsoft Entra ID, one of the [free services](https://azure.microsoft.com/free/) in the Azure cloud platform. |
+If you require permissioned access over content in search results, there's a technique for applying filters that include or exclude documents based on user identity. This workaround adds a string field in the data source that represents a group or user identity, which you can make filterable in your index. For more information about this pattern, see [Security trimming based on identity filters](search-security-trimming-for-azure-search.md).
 
 ## Data residency
 

articles/search/search-security-trimming-for-azure-search-with-aad.md

@@ -12,6 +12,7 @@ ms.date: 02/15/2024
 ms.custom:
   - devx-track-csharp
   - ignite-2023
+ROBOTS: NOINDEX,NOFOLLOW
 ---
 # Security filters for trimming Azure AI Search results using Microsoft Entra tenants and identities
 

articles/search/search-security-trimming-for-azure-search.md

@@ -39,7 +39,7 @@ A better solution is using the `search.in` function for security filters, as des
 
 ## Prerequisites
 
-* A string field containing a group or user identity. It must have the filterable attribute. It should be a collection. It shouldn't allow nulls.
+* A string field containing a group or user identity, such as a Microsoft Entra object identifier.
 
 * Other fields in the same document should provide the content that's accessible to that group or user. In the following JSON documents, the "security_id" fields contain identities used in a security filter, and the name, salary, and marital status are included if the identity of the caller matches the "security_id" of the document.
 
@@ -50,14 +50,14 @@ A better solution is using the `search.in` function for security filters, as des
             "name": "Abram",   
             "salary": 75000,   
             "married": true,
-            "security_id": "10011"
+            "security_id": "alphanumeric-object-id-for-employee-1"
         },
         "Employee-2": {  
             "employee_id": "200-2000-20-2-20000-2",
             "name": "Adams",   
             "salary": 75000,   
             "married": true,
-            "security_id": "20022"
+            "security_id": "alphanumeric-object-id-for-employee-2"
         } 
     }  
     ```
@@ -66,7 +66,9 @@ A better solution is using the `search.in` function for security filters, as des
 
 In the search index, within the fields collection, you need one field that contains the group or user identity, similar to the fictitious "security_id" field in the previous example.
 
-1. Add a security field as a `Collection(Edm.String)`. Make sure it has a `filterable` attribute set to `true`. In this example, the security field is `group_ids`. It's populated with the string `["group_id1, group_id2"]`. 
+1. Add a security field as a `Collection(Edm.String)`.
+
+1. Set the field's `filterable` attribute set to `true`.
 
 1. Set the field's `retrievable` attribute to `false` so that it isn't returned as part of the search request.
 
@@ -187,7 +189,6 @@ This article describes a pattern for filtering results based on user identity an
 
 For more examples, demos, and videos:
 
-* [Configure document security for a chat app in Python](/azure/developer/python/get-started-app-chat-document-security-trim)
-* [Set up a security filter using Microsoft Entra ID](search-security-trimming-for-azure-search-with-aad.md)
+* [Get started with chat document security in Python](/azure/developer/python/get-started-app-chat-document-security-trim)
 * [Set up optional sign in and document level access control (modifications to the AzureOpenAIDemo app)](https://github.com/Azure-Samples/azure-search-openai-demo/blob/main/docs/login_and_acl.md)
 * [Video: Secure your Intelligent Applications with Microsoft Entra](https://build.microsoft.com/en-US/sessions/b5636ca7-64c2-493c-9b30-4a35852acfbe?source=/speakers/cc9b56a0-4af0-4b60-a2f3-8312c5b35ca2)