Merge pull request #5245 from eric-urban/eur/pii-filter-hold

pull back PII flter info

Author: JillGrant615

articles/ai-services/.openpublishing.redirection.ai-services.json

@@ -1169,7 +1169,11 @@
       "source_path_from_root": "/articles/ai-services/content-understanding/concepts/accuracy-confidence.md",
       "redirect_url": "/azure/ai-services/content-understanding/concepts/best-practices",
       "redirect_document_id": true
+    },
+    {
+      "source_path_from_root": "/articles/ai-services/openai/concepts/content-filter-personal-information.md",
+      "redirect_url": "/azure/ai-services/openai/concepts/content-filter",
+      "redirect_document_id": false
     }
-
   ]
 }

articles/ai-services/openai/concepts/content-filter-personal-information.md

@@ -6,7 +6,7 @@ author: PatrickFarley
 ms.author: pafarley
 ms.service: azure-ai-openai
 ms.topic: conceptual
-ms.date: 05/07/2025
+ms.date: 5/28/2025
 ms.custom: template-concept, devx-track-python
 manager: nitinme
 ---
@@ -47,7 +47,6 @@ The following table summarizes the risk categories supported by Azure OpenAI's c
 | [Groundedness](/azure/ai-services/openai/concepts/content-filter-groundedness)<sup>2</sup> | Groundedness detection flags whether the text responses of large language models (LLMs) are grounded in the source materials provided by the users. Ungrounded material refers to instances where the LLMs produce information that is non-factual or inaccurate from what was present in the source materials. Requires [document embedding and formatting](./content-filter-document-embedding.md). |
 | [Protected Material for Text](/azure/ai-services/openai/concepts/content-filter-protected-material)<sup>1</sup> | Protected material text describes known text content (for example, song lyrics, articles, recipes, and selected web content) that can be outputted by large language models.|
 | [Protected Material for Code](/azure/ai-services/openai/concepts/content-filter-protected-material) | Protected material code describes source code that matches a set of source code from public repositories, which can be outputted by large language models without proper citation of source repositories.|
-| [Personally identifiable information (PII)](/azure/ai-services/openai/concepts/content-filter-personal-information) | Personally identifiable information (PII) refers to any information that can be used to identify a particular individual. PII detection involves analyzing text content in LLM completions and filtering any PII that was returned. |
 
 <sup>1</sup> If you're an owner of text material and want to submit text content for protection, [file a request](https://aka.ms/protectedmaterialsform).
 

articles/ai-services/openai/concepts/content-filter.md

@@ -5,7 +5,7 @@ description: Learn how to use and configure the content filters that come with A
 manager: nitinme
 ms.service: azure-ai-openai
 ms.topic: how-to
-ms.date: 03/05/2025
+ms.date: 5/28/2025
 author: mrbullwinkle
 ms.author: mbullwin
 recommendations: false
@@ -48,8 +48,6 @@ You can configure the following filter categories in addition to the default har
 | Protected material - code |GA| On | Completion | Filters protected code or gets the example citation and license information in annotations for code snippets that match any public code sources, powered by GitHub Copilot. For more information about consuming annotations, see the [Protected material concepts guide](/azure/ai-services/openai/concepts/content-filter-protected-material) |
 | Protected material - text | GA| On | Completion | Identifies and blocks known text content from being displayed in the model output (for example, song lyrics, recipes, and selected web content).  |
 | Groundedness | Preview |Off | Completion |Detects whether the text responses of large language models (LLMs) are grounded in the source materials provided by the users. Ungroundedness refers to instances where the LLMs produce information that is non-factual or inaccurate from what was present in the source materials. Requires: [Document embedding and formatting](/azure/ai-services/openai/concepts/content-filter?tabs=warning%2Cuser-prompt%2Cpython-new#embedding-documents-in-your-prompt).|
-| Personally identifiable information (PII) | Preview | Off | Completion | Filters information that can be used to identify a particular individual, such as a name, address, phone number, email address, social security number, driver's license number, passport number, or similar information. |
-
 
 [!INCLUDE [create-content-filter](../../../ai-foundry/includes/create-content-filter.md)]
 

articles/ai-services/openai/how-to/content-filters.md

@@ -68,8 +68,6 @@ items:
         href: ./concepts/content-filter-groundedness.md
       - name: Protected material detection
         href: ./concepts/content-filter-protected-material.md
-      - name: Personally identifiable information (PII) detection
-        href: ./concepts/content-filter-personal-information.md
       - name: Content filter configurability
         href: ./concepts/content-filter-configurability.md
       - name: Content filter annotations

articles/ai-services/openai/toc.yml

@@ -20,10 +20,6 @@ This article provides a summary of the latest releases and major documentation u
 
 ## May 2025
 
-### PII detection content filter
-
-Personally identifiable information (PII) detection is now available as a built-in content filter. This feature allows you to identify and block sensitive information in LLM outputs, enhancing data privacy. For more information, see the [PII detection](./concepts/content-filter-personal-information.md) documentation.
-
 ### Spotlighting for prompt shields
 
 Spotlighting is a sub-feature of prompt shields that enhances protection against indirect (embedded document) attacks by tagging input documents with special formatting to indicate lower trust to the model. For more information, see the [Prompt shields filter](./concepts/content-filter-prompt-shields.md) documentation.