Switch Foundry Tools back to AI services when general services are intended

Author: JKirsch1

articles/azure-netapp-files/azure-netapp-files-solution-architectures.md

@@ -225,7 +225,7 @@ This section provides references for High Performance Computing (HPC) solutions.
 
 This section provides solutions for Azure platform services. 
 
-### Azure data and Foundry Tools
+### Azure data and AI services
 
 * [How Azure NetApp Files Object REST API powers Azure and ISV Data and AI services](https://techcommunity.microsoft.com/blog/azurearchitectureblog/how-azure-netapp-files-object-rest-api-powers-azure-and-isv-data-and-ai-services/4459545)
 * [Unlocking Advanced Data Analytics and AI with Azure NetApp Files object REST API](https://techcommunity.microsoft.com/blog/azurearchitectureblog/unlocking-advanced-data-analytics--ai-with-azure-netapp-files-object-rest-api/4486098)

articles/azure-netapp-files/object-rest-api-onelake.md

@@ -12,7 +12,7 @@ ms.author: anfdocs
 
 With Azure NetApp Files object REST API, you can use [OneLake shortcuts](/fabric/onelake/onelake-shortcuts) to virtualize Azure NetApp Files into Microsoft Fabric's unified data lake. With this shortcut, Azure AI search can index and retrieve data directly from Azure NetApp Files for semantic search and vector-based retrieval with intelligent applications. 
 
-Sensitive information is protected through Microsoft Virtual Networks and Azure NetApp Files robust security protocols when you create the OneLake shortcut. A virtual data lake design approach simplifies data sharing across Azure's Data and Foundry Tools, improving collaboration. 
+Sensitive information is protected through Microsoft Virtual Networks and Azure NetApp Files robust security protocols when you create the OneLake shortcut. A virtual data lake design approach simplifies data sharing across Azure's Data and AI services, improving collaboration. 
 
 ## Before you begin 
 

articles/security/fundamentals/shared-responsibility-ai.md

@@ -68,7 +68,7 @@ To learn more about the unique nature of AI threat testing, read how [Microsoft
 ## Configure before customize
 Microsoft recommends organizations start with SaaS based approaches  like the Copilot model for their initial adoption of AI and for all subsequent AI workloads. This minimizes the level of responsibility and expertise your organization has to provide to design, operate, and secure these highly complex capabilities.
 
-If the current "off the shelf" capabilities don't meet the specific needs for a workload, you can adopt a PaaS model by using Foundry Tools, such as [Azure OpenAI Service](/azure/ai-services/openai/overview), to meet those specific requirements.
+If the current "off the shelf" capabilities don't meet the specific needs for a workload, you can adopt a PaaS model by using AI services, such as [Azure OpenAI Service](/azure/ai-services/openai/overview), to meet those specific requirements.
 
 Custom model building should only be adopted by organizations with deep expertise in data science and the security, privacy, and ethical considerations of AI.
 

articles/security/fundamentals/shared-responsibility.md

@@ -74,7 +74,7 @@ Microsoft is responsible for the underlying cloud infrastructure, which includes
 
 ## AI shared responsibility
 
-When using Foundry Tools, the shared responsibility model introduces unique considerations beyond traditional IaaS, PaaS, and SaaS. Microsoft is responsible for securing the AI infrastructure, model hosting, and platform-level safeguards. Customers, however, remain accountable for how AI is applied within their environment—this includes protecting sensitive data, managing prompt security, mitigating prompt injection risks, and ensuring compliance with organizational and regulatory requirements.
+When using AI services, the shared responsibility model introduces unique considerations beyond traditional IaaS, PaaS, and SaaS. Microsoft is responsible for securing the AI infrastructure, model hosting, and platform-level safeguards. Customers, however, remain accountable for how AI is applied within their environment—this includes protecting sensitive data, managing prompt security, mitigating prompt injection risks, and ensuring compliance with organizational and regulatory requirements.
 
 Because responsibilities differ significantly for AI workloads, you should review the [AI Shared Responsibility Model](/azure/security/fundamentals/shared-responsibility-ai) for detailed guidance on roles, best practices, and risk management.