foundry freshness

Author: PatrickFarley

articles/ai-studio/ai-services/content-safety-overview.md

@@ -7,16 +7,16 @@ ms.service: azure-ai-foundry
 ms.custom:
   - ignite-2024
 ms.topic: overview
-ms.date: 11/09/2024
+ms.date: 02/20/2025
 ms.author: pafarley
 author: PatrickFarley
 ---
 
-# Content safety in the Azure AI Foundry portal
+# Content Safety in the Azure AI Foundry portal
 
 Azure AI Content Safety is an AI service that detects harmful user-generated and AI-generated content in applications and services. Azure AI Content Safety includes various APIs that allow you to detect and prevent the output of harmful content. The interactive Content Safety **try out** page in [Azure AI Foundry portal](https://ai.azure.com) allows you to view, explore, and try out sample code for detecting harmful content across different modalities. 
 
-## Features 
+## Features
 
 You can use Azure AI Content Safety for many scenarios: 
 
@@ -44,6 +44,6 @@ You can use Azure AI Content Safety for many scenarios:
 Refer to the [Content Safety overview](/azure/ai-services/content-safety/overview) for supported regions, rate limits, and input requirements for all features. Refer to the [Language support](/azure/ai-services/content-safety/language-support) page for supported languages. 
 
 
-## Next step 
+## Next step
 
 Get started using Azure AI Content Safety in [Azure AI Foundry portal](https://ai.azure.com) by following the [How-to guide](./how-to/content-safety.md).

articles/ai-studio/how-to/use-blocklists.md

@@ -7,7 +7,7 @@ ms.service: azure-ai-foundry
 ms.custom:
   - ignite-2024
 ms.topic: how-to
-ms.date: 11/07/2024
+ms.date: 02/20/2025
 ms.author: pafarley
 author: PatrickFarley
 ---

articles/ai-studio/includes/use-blocklists.md

@@ -6,7 +6,7 @@ ms.reviewer: pafarley
 ms.author: pafarley
 ms.service: azure-ai-foundry
 ms.topic: include
-ms.date: 12/05/2024
+ms.date: 02/20/2025
 ms.custom: include
 ---
 
@@ -17,19 +17,19 @@ ms.custom: include
 
     :::image type="content" source="../media/content-safety/content-filter/select-blocklists.png" lightbox="../media/content-safety/content-filter/select-blocklists.png" alt-text="Screenshot of the Blocklists page tab.":::
 
-2. Select **Create a blocklist**. Enter a name for your blocklist, add a description, and select an Azure OpenAI resource to connect it to. Then select **Create Blocklist**.
+1. Select **Create a blocklist**. Enter a name for your blocklist, add a description, and select an Azure OpenAI resource to connect it to. Then select **Create Blocklist**.
 
-3. Select your new blocklist once it's created. On the blocklist's page, select **Add new term**.
+1. Select your new blocklist once it's created. On the blocklist's page, select **Add new term**.
 
-4. Enter the term that should be filtered and select **Add term**. You can also use a regex. You can delete each term in your blocklist.
+1. Enter the term that should be filtered and select **Add term**. You can also use a regex. You can delete each term in your blocklist.
 
 ## Attach a blocklist to a content filter configuration
 
 1. Once the blocklist is ready, go back to the **Safety+ Security** page and select the **Content filters** tab. Create a new content filter configuration. This opens a wizard with several AI content safety components.
 
     :::image type="content" source="../media/content-safety/content-filter/create-content-filter.png" lightbox="../media/content-safety/content-filter/create-content-filter.png" alt-text="Screenshot of the Create content filter button.":::
 
-2. On the **Input filter** and **Output filter** screens, toggle the **Blocklist** button on. You can then select a blocklist from the list. 
+1. On the **Input filter** and **Output filter** screens, toggle the **Blocklist** button on. You can then select a blocklist from the list. 
     There are two types of blocklists: the custom blocklists you created, and prebuilt blocklists that Microsoft provides—in this case a Profanity blocklist (English).
 
-3. You can now decide which of the available blocklists you want to include in your content filtering configuration. The last step is to review and finish the content filtering configuration by selecting **Next**. You can always go back and edit your configuration. Once it’s ready, select a **Create content filter**. The new configuration that includes your blocklists can now be applied to a deployment.
+1. You can now decide which of the available blocklists you want to include in your content filtering configuration. The last step is to review and finish the content filtering configuration by selecting **Next**. You can always go back and edit your configuration. Once it’s ready, select a **Create content filter**. The new configuration that includes your blocklists can now be applied to a deployment.

articles/ai-studio/responsible-use-of-ai-overview.md

@@ -1,30 +1,30 @@
 ---
 title: Responsible AI for Azure AI Foundry
 titleSuffix: Azure AI Foundry
-description: Learn how to use AI responsibly with Azure AI Foundry.
+description: Learn how to use AI services and features responsibly with Azure AI Foundry.
 manager: nitinme
 keywords: Azure AI services, cognitive
 ms.service: azure-ai-foundry
 ms.topic: overview
-ms.date: 11/06/2024
+ms.date: 02/20/2025
 ms.author: pafarley
 author: PatrickFarley
 ms.custom: ignite-2024
 ---
 
 # Responsible AI for Azure AI Foundry
 
-This article aims to provide an overview of the resources available to help you use AI responsibly. Our recommended essential development steps are grounded in the [Microsoft Responsible AI Standard](https://aka.ms/RAI), which sets policy requirements that our own engineering teams follow. Much of the content of the Standard follows a pattern, asking teams to Identify, Measure, and Mitigate potential content risks, and plan for how to Operate the AI system as well.  
+This article provides an overview of the resources available to help you use AI responsibly. Our recommended essential development steps are grounded in the [Microsoft Responsible AI Standard](https://aka.ms/RAI), which sets policy requirements that our own engineering teams follow. Much of the content of the Standard follows a pattern, asking teams to Identify, Measure, and Mitigate potential content risks, and plan for how to Operate the AI system as well.  
 
 At Microsoft, our approach is guided by a governance framework rooted in AI principles, which establish product requirements and serve as our "north star." When we identify a business use case for generative AI, we first assess the potential risks of the AI system to pinpoint critical focus areas. 
 
 Once we identify these risks, we evaluate their prevalence within the AI system through systematic measurement, helping us prioritize areas that need attention. We then apply appropriate mitigations and measure again to assess effectiveness. 
 
 Finally, we examine strategies for managing risks in production, including deployment and operational readiness and setting up monitoring to support ongoing improvement after the application is live. 
 
-:::image type="content" source="media/content-safety/safety-pattern.png" alt-text="Diagram of the content safety pattern.":::
+:::image type="content" source="media/content-safety/safety-pattern.png" alt-text="Diagram of the content safety pattern: Map, Measure, and Manage.":::
 
-In alignment with those Microsoft's RAI practices, these recommendations are organized into four stages:
+In alignment with Microsoft's RAI practices, these recommendations are organized into four stages:
 - **Map**: Identify and prioritize potential content risks that could result from your AI system through iterative red-teaming, stress-testing, and analysis. 
 - **Measure**: Measure the frequency and severity of those content risks by establishing clear metrics, creating measurement test sets, and completing iterative, systematic testing (both manual and automated). 
 - **Mitigate**: Mitigate content risks by implementing tools and strategies such as prompt engineering and using our content filters. Repeat measurement to test effectiveness after implementing mitigations.