Merge branch 'main' into release-2025-openai-feb-latest

Author: v-alje

articles/ai-foundry/model-inference/overview.md

@@ -59,7 +59,7 @@ At Microsoft, we're committed to the advancement of AI driven by principles that
 Microsoft helps guard against abuse and unintended harm by taking the following actions:
 
 - Incorporating Microsoft's [principles for responsible AI use](https://www.microsoft.com/ai/responsible-ai)
-- Adopting a [code of conduct](/legal/cognitive-services/openai/code-of-conduct?context=/azure/ai-services/openai/context/context) for use of the service
+- Adopting a [code of conduct](/legal/ai-code-of-conduct?context=/azure/ai-services/openai/context/context) for use of the service
 - Building [content filters](/azure/ai-services/content-safety/overview) to support customers
 - Providing responsible AI [information and guidance](/legal/cognitive-services/openai/transparency-note?context=%2Fazure%2Fai-services%2Fopenai%2Fcontext%2Fcontext&tabs=image) that customers should consider when using Azure OpenAI.
 

articles/ai-services/agents/how-to/virtual-networks.md

@@ -77,7 +77,7 @@ Both services enable you to build agents using the same API and SDKs, but if you
 
 ## Responsible AI
 
-At Microsoft, we're committed to the advancement of AI driven by principles that put people first. Generative models such as the ones available in Azure OpenAI have significant potential benefits, but without careful design and thoughtful mitigations, such models have the potential to generate incorrect or even harmful content. Microsoft has made significant investments to help guard against abuse and unintended harm, which includes incorporating Microsoft’s <a href="https://www.microsoft.com/ai/responsible-ai?activetab=pivot1:primaryr6" target="_blank">principles for responsible AI use</a>, adopting a [Code of Conduct](/legal/cognitive-services/openai/code-of-conduct?context=/azure/ai-services/openai/context/context) for use of the service, building [content filters](/azure/ai-services/content-safety/overview) to support customers, and providing responsible AI [information and guidance](/legal/cognitive-services/openai/transparency-note?context=%2Fazure%2Fai-services%2Fopenai%2Fcontext%2Fcontext&tabs=image) that customers should consider when using Azure AI Agent Service.
+At Microsoft, we're committed to the advancement of AI driven by principles that put people first. Generative models such as the ones available in Azure OpenAI have significant potential benefits, but without careful design and thoughtful mitigations, such models have the potential to generate incorrect or even harmful content. Microsoft has made significant investments to help guard against abuse and unintended harm, which includes incorporating Microsoft’s <a href="https://www.microsoft.com/ai/responsible-ai?activetab=pivot1:primaryr6" target="_blank">principles for responsible AI use</a>, adopting a [Code of Conduct](/legal/ai-code-of-conduct?context=/azure/ai-services/agents/context/context) for use of the service, building [content filters](/azure/ai-services/content-safety/overview) to support customers, and providing responsible AI [information and guidance](/legal/cognitive-services/openai/transparency-note?context=%2Fazure%2Fai-services%2Fopenai%2Fcontext%2Fcontext&tabs=image) that customers should consider when using Azure AI Agent Service.
 
 ## Get started with Azure AI Agent Service
 

articles/ai-services/agents/overview.md

@@ -52,6 +52,8 @@ items:
           href: concepts/tracing.md
         - name: Content filtering
           href: ../openai/how-to/content-filters.md?context=/azure/ai-services/agents/context/context
+        - name: Use virtual networks
+          href: how-to/virtual-networks.md
 - name: Responsible AI
   items:
     - name: Transparency note

articles/ai-services/agents/toc.yml

@@ -18,7 +18,7 @@ feedback_help_link_url: https://learn.microsoft.com/answers/tags/156/azure-face
 
 # Abuse monitoring in Face liveness detection
 
-Azure AI Face liveness detection lets you detect and mitigate instances of recurring content and/or behaviors that indicate a violation of the [Code of Conduct](/legal/cognitive-services/face/code-of-conduct?context=/azure/ai-services/computer-vision/context/context) or other applicable product terms. This guide shows you how to work with these features to ensure your application is compliant with Azure policy.
+Azure AI Face liveness detection lets you detect and mitigate instances of recurring content and/or behaviors that indicate a violation of the [Code of Conduct](/legal/ai-code-of-conduct?context=/azure/ai-services/computer-vision/context/context) or other applicable product terms. This guide shows you how to work with these features to ensure your application is compliant with Azure policy.
 
 Details on how data is handled can be found on the [Data, Privacy, and Security](/legal/cognitive-services/face/data-privacy-security?context=/azure/ai-services/computer-vision/context/context) page.
 

articles/ai-services/computer-vision/concept-liveness-abuse-monitoring.md

@@ -350,7 +350,7 @@ items:
     - name: Best practices for enrolling users
       href: enrollment-overview.md
     - name: Code of conduct
-      href: /legal/cognitive-services/face/code-of-conduct?context=/azure/ai-services/computer-vision/context/context
+      href: /legal/ai-code-of-conduct?context=/azure/ai-services/computer-vision/context/context
     - name: Data privacy and security
       href: /legal/cognitive-services/face/data-privacy-security?context=/azure/ai-services/computer-vision/context/context
     - name: Limited Access features

articles/ai-services/computer-vision/toc.yml

@@ -73,7 +73,7 @@ See [Quickstart](quickstart/use-ai-foundry.md) for more examples.
 
 
 ## Responsible AI
- Azure AI Content Understanding is designed to guard against processing harmful content, such as graphic violence and gore, hateful speech and bullying, exploitation, abuse, and more. For more information and a full list of prohibited content, *see* our [**Transparency note**](/legal/cognitive-services/content-understanding/transparency-note?toc=/azure/ai-services/content-understanding/toc.json&bc=/azure/ai-services/content-understanding/breadcrumb/toc.json) and our [**Code of Conduct**](/legal/cognitive-services/openai/code-of-conduct).
+ Azure AI Content Understanding is designed to guard against processing harmful content, such as graphic violence and gore, hateful speech and bullying, exploitation, abuse, and more. For more information and a full list of prohibited content, *see* our [**Transparency note**](/legal/cognitive-services/content-understanding/transparency-note?toc=/azure/ai-services/content-understanding/toc.json&bc=/azure/ai-services/content-understanding/breadcrumb/toc.json) and our [**Code of Conduct**](https://aka.ms/AI-CoC).
 
 ### Modified Content Filtering
 

articles/ai-services/content-understanding/overview.md

@@ -13,13 +13,13 @@ manager: nitinme
 
 # Abuse Monitoring
 
-Azure OpenAI Service detects and mitigates instances of recurring content and/or behaviors that suggest use of the service in a manner that might violate the [Code of Conduct](/legal/cognitive-services/openai/code-of-conduct?context=/azure/ai-services/openai/context/context). Details on how data is handled can be found on the [Data, Privacy, and Security](/legal/cognitive-services/openai/data-privacy?context=/azure/ai-services/openai/context/context) page.
+Azure OpenAI Service detects and mitigates instances of recurring content and/or behaviors that suggest use of the service in a manner that might violate the [Code of Conduct](https://aka.ms/AI-CoC). Details on how data is handled can be found on the [Data, Privacy, and Security](/legal/cognitive-services/openai/data-privacy?context=/azure/ai-services/openai/context/context) page.
 
 ## Components of abuse monitoring
 
 There are several components to abuse monitoring:
 
-- **Content Classification**: Classifier models detect harmful text and/or images in user prompts (inputs) and completions (outputs). The system looks for categories of harms as defined in the [Content Requirements](/legal/cognitive-services/openai/code-of-conduct?context=/azure/ai-services/openai/context/context), and assigns severity levels as described in more detail on the [Content Filtering](/azure/ai-services/openai/concepts/content-filter) page. The content classification signals contribute to pattern detection as described below.  
+- **Content Classification**: Classifier models detect harmful text and/or images in user prompts (inputs) and completions (outputs). The system looks for categories of harms as defined in the [Content Requirements](/legal/ai-code-of-conduct?context=/azure/ai-services/openai/context/context), and assigns severity levels as described in more detail on the [Content Filtering](/azure/ai-services/openai/concepts/content-filter) page. The content classification signals contribute to pattern detection as described below.  
 - **Abuse Pattern Capture**: Azure OpenAI Service’s abuse monitoring system looks at customer usage patterns and employs algorithms and heuristics to detect and score indicators of potential abuse. Detected patterns consider, for example, the frequency and severity at which harmful content is detected (as indicated in content classifier signals) in a customer’s prompts and completions, as well as the intentionality of the behavior. The trends and urgency of the detected pattern will also affect scoring of potential abuse severity.
     For example, a higher volume of harmful content classified as higher severity, or recurring conduct indicating intentionality (such as recurring jailbreak attempts) are both more likely to receive a high score indicating potential abuse. 
 - **Review and Decision**: Prompts and completions that are flagged through content classification and/or identified as part of a potentially abusive pattern of use are subjected to another review process to help confirm the system’s analysis and inform actioning decisions for abuse monitoring. Such review is conducted through two methods: automated review and human review.

articles/ai-services/openai/concepts/abuse-monitoring.md

@@ -123,7 +123,8 @@ curl -X PUT "https://management.azure.com/subscriptions/<SUBSCRIPTION>/resourceG
 
 #### Deleting a Provisioned Managed deployment
 
-To delete a deployment, use the [Deployments - Delete REST API(/rest/api/aiservices/accountmanagement/deployments/delete?view=rest-aiservices-accountmanagement-2024-10-01&tabs=HTTP&preserve-view=true) and send an HTTP DELETE to the deployment resource. Like with creating deployments, you must include the following parameters:
+To delete a deployment, use the [Deployments - Delete REST API](/rest/api/aiservices/accountmanagement/deployments/delete?view=rest-aiservices-accountmanagement-2024-10-01&tabs=HTTP&preserve-view=true) and send an HTTP DELETE to the deployment resource. Like with creating deployments, you must include the following parameters:
+
 
 - Azure subscription id
 - Azure resource group name

articles/ai-services/openai/how-to/fine-tuning.md

@@ -30,4 +30,4 @@ Configurable content filters for inputs (prompts) and outputs (completions) are
 
 Content filtering configurations are created within a Resource in Azure AI Foundry portal, and can be associated with Deployments. [Learn more about configurability here](../how-to/content-filters.md).  
 
-Customers are responsible for ensuring that applications integrating Azure OpenAI comply with the [Code of Conduct](/legal/cognitive-services/openai/code-of-conduct?context=%2Fazure%2Fai-services%2Fopenai%2Fcontext%2Fcontext). 
+Customers are responsible for ensuring that applications integrating Azure OpenAI comply with the [Code of Conduct](/legal/ai-code-of-conduct?context=/azure/ai-services/openai/context/context).