Address reviewer feedback

Author: Orin-Thomas

learn-pr/advocates/govern-AI-services-Azure-Policy/1-azure-policy-ai-services.yml

@@ -2,7 +2,7 @@
 uid: learn.govern-ai-services-azure-policy.azure-policy-ai-services
 title: Azure Policy and AI Services
 metadata:
-  title: Azure Policy and AI Services
+  title: Azure Policy and AI services
   description: Learn about Azure Policies related to AI services.
   ms.date: 06/26/2025
   author: Orin-Thomas

learn-pr/advocates/govern-AI-services-Azure-Policy/3-azure-ai-foundry-azure-machine-learning-policies.yml

@@ -2,7 +2,7 @@
 uid: learn.govern-ai-services-azure-policy.azure-ai-foundry-azure-machine-learning-policies
 title: Azure AI Foundry and Azure Machine Learning Policies
 metadata:
-  title: Azure AI Foundry and Azure Machine Learning Policies
+  title: Azure AI Foundry and Azure Machine Learning policies
   description: Learn about policies related to Azure AI Foundry and Azure Machine Learning.
   ms.date: 06/26/2025
   author: Orin-Thomas

learn-pr/advocates/govern-AI-services-Azure-Policy/4-knowledge-check.yml

@@ -2,7 +2,7 @@
 uid: learn.govern-ai-services-azure-policy.knowledge-check
 title: Knowledge Check
 metadata:
-  title: Knowledge Check
+  title: Knowledge check
   description: Check your knowledge.
   ms.date: 06/26/2025
   author: Orin-Thomas

learn-pr/advocates/govern-AI-services-Azure-Policy/includes/1-azure-policy-ai-services.md

@@ -10,11 +10,11 @@ As an AI workload operations administrator, Azure it's your responsibility to en
 - Azure AI Foundry: Azure AI Foundry is a cloud service for the full lifecycle of application development. With AI Foundry developers can explore, build, test, and deploy AI tools and ML models.
 - Azure Machine Learning: Azure Machine Learning is a cloud service for accelerating and managing the machine learning (ML) project lifecycle.
 
-Policy definitions or initiatives have a scope to which they're applied to. When you apply the scope of a Resource Group (or another scope) to a definition or initiative, the JSON file for the underlying policy definition targets resources based on the resource type.
+Policy definitions or initiatives have a scope to which they're applied. When you apply the scope of a Resource Group (or another scope) to a definition or initiative, the JSON file for the underlying policy definition targets resources based on the resource type.
 
 Here's an example definition for an audit policy to enable the usage of Private Endpoint for Azure AI services on GitHub:
 
-![A screenshot of the Azure portal, with an Azure AI services policy related to private endpoints displayed.](../media/ai-services-policy.svg)
+[![A screenshot of the Azure portal, with an Azure AI services policy related to private endpoints displayed.](../media/ai-services-policy.svg)](../media/ai-services-policy-big.svg#lightbox)
 
 This definition runs under its scope against any resource of type Microsoft.CognitiveServices. When you analyze the resource ID structure of a service such as Azure OpenAI, you can see the resource type: /subscriptions/\<subscriptionID>/resourceGroups/\<RG-name>/providers/Microsoft.CognitiveServices/accounts/ContosoAIDemo
 
@@ -25,7 +25,7 @@ The result is this resource (Azure OpenAI) will be affected by the policy defini
 
 Here's an example definition for an audit policy to deploy a private endpoint to Azure Machine Learning:
 
-![A screenshot of the Azure portal, with an Azure Machine Learninga policy related to private endpoints displayed.](../media/machine-learning-policy.svg)
+[![A screenshot of the Azure portal, with an Azure Machine Learning policy related to private endpoints displayed.](../media/machine-learning-policy.svg)](../media/machine-learning-policy-big.svg#lightbox)
 
 The policy definition targets resources of type "Microsoft.MachineLearningServices". When you analyze the resource ID structure of BOTH Azure Machine Learning and Azure AI Foundry, you can see the same structure:
 

learn-pr/advocates/govern-AI-services-Azure-Policy/includes/2-assess-ai-workload-compliance-with-azure-policy.md

@@ -16,7 +16,7 @@ To apply a policy definition to an Azure AI service:
 1. Select the policy Azure AI Services resources should restrict network access.
 1. On the policy page, scroll down to check which resource is in noncompliant state:
 
-- ![A screenshot of the Azure portal showing the results of a policy assessment.](../media/policy-assessment.svg)
+[![A screenshot of the Azure portal showing the results of a policy assessment](../media/policy-assessment.svg)](../media/policy-assessment-big.svg#lightbox)
 
 Azure Policy evaluates the state of a resource to enforce organizational standards and to assess compliance. You can apply the following list of built-in policies to Azure AI services directly from the Azure portal:
 

learn-pr/advocates/govern-AI-services-Azure-Policy/includes/3-azure-ai-foundry-azure-machine-learning-policies.md

@@ -20,7 +20,7 @@ While cross-compatible, Microsoft provides built-in policies for both services.
 | **Configure Azure Machine Learning workspace to use private DNS zones** | Use private DNS zones to override the DNS resolution for a private endpoint. A private DNS zone links to your virtual network to resolve to Azure Machine Learning workspaces. |
 | **Configure Azure Machine Learning Workspaces to disable public network access** | Disable public network access for Azure Machine Learning Workspaces so that your workspaces aren't accessible over the public internet. This helps protect the workspaces against data leakage risks. You can control exposure of your workspaces by creating private endpoints instead. |
 | **Configure Azure Machine Learning workspaces with private endpoints** | Private endpoints connect your virtual network to Azure services without a public IP address at the source or destination. By mapping private endpoints to your Azure Machine Learning workspace, you can reduce data leakage risks. |
-| **Configure diagnostic settings for Azure Machine Learning Workspaces to Log Analytics workspace** | Deploys the diagnostic settings for Azure Machine Learning Workspaces to stream resource logs to a Log Analytics Workspace when any Azure Machine Learning Workspace which is missing this diagnostic settings is created or updated. |
+| **Configure diagnostic settings for Azure Machine Learning Workspaces to Log Analytics workspace** | Deploys the diagnostic settings for Azure Machine Learning Workspaces to stream resource logs to a Log Analytics Workspace when any Azure Machine Learning Workspace which is missing these diagnostic settings is created or updated. |
 | **Resource logs in Azure Machine Learning Workspaces should be enabled** | Resource logs enable recreating activity trails to use for investigation purposes when a security incident occurs or when your network is compromised. |
 
 Below is the list of built-in policies you can apply to Azure AI Foundry directly from the Azure portal:

learn-pr/advocates/govern-AI-services-Azure-Policy/includes/5-summary.md

@@ -1,6 +1,6 @@
 In this module, you learned how to set up policy and initiative assignments to Azure AI services, Azure AI Foundry, and Azure Machine Learning and how to identify noncompliant AI resources in Azure.
 
-## Learn more:
+## Learn more
 
 - [Create a custom policy definition.](/azure/governance/policy/tutorials/create-custom-policy-definition)
 - [Azure Policy built-in policy definitions for Azure AI services](/azure/ai-services/policy-reference)

learn-pr/advocates/govern-AI-services-Azure-Policy/index.yml

@@ -9,7 +9,7 @@ metadata:
   ms.service: azure-policy
   ms.topic: module
   ms.collection: ce-advocates-ai-copilot
-title: Govern AI Services with Azure Policy
+title: Govern AI services with Azure Policy
 summary: TBD
 abstract: |
     After completing this module, you'll be able to: