update connections

Author: sdgilley

articles/ai-studio/concepts/connections.md

@@ -31,30 +31,30 @@ As another example, you can [create a connection](../how-to/connections-add.md)
 
 ## Connections to non-Microsoft services
 
-Azure AI Foundry supports connections to non-Microsoft services, including the following:
-- The [API key connection](../how-to/connections-add.md) handles authentication to your specified target on an individual basis. This is the most common non-Microsoft connection type.
-- The [custom connection](../how-to/connections-add.md) allows you to securely store and access keys while storing related properties, such as targets and versions. Custom connections are useful when you have many targets that or cases where you wouldn't need a credential to access. LangChain scenarios are a good example where you would use custom service connections. Custom connections don't manage authentication, so you'll have to manage authentication on your own.
+Azure AI Foundry supports connections to non-Microsoft services, including:
+- The [API key connection](../how-to/connections-add.md) handles authentication to your specified target on an individual basis. API key is the most common non-Microsoft connection type.
+- The [custom connection](../how-to/connections-add.md) allows you to securely store and access keys while storing related properties, such as targets and versions. Custom connections are useful when you have many targets that or cases where you wouldn't need a credential to access. LangChain scenarios are a good example where you would use custom service connections. Custom connections don't manage authentication, so you have to manage authentication on your own.
 
 ## Connections to datastores
 
 > [!IMPORTANT]
-> Data connections cannot be shared across projects. They are created exclusively in the context of one project. 
+> Data connections can't be shared across projects. They're created exclusively in the context of one project. 
 
 Creating a data connection allows you to access external data without copying it to your project. Instead, the connection provides a reference to the data source.
 
 A data connection offers these benefits:
 
 - A common, easy-to-use API that interacts with different storage types including Microsoft OneLake, Azure Blob, and Azure Data Lake Gen2.
 - Easier discovery of useful connections in team operations.
-- For credential-based access (service principal/SAS/key), Azure AI Foundry connection secures credential information. This way, you won't need to place that information in your scripts.
+- Credential-based access (service principal/SAS/key). Azure AI Foundry connection secures credential information so you don't need to place that information in your scripts.
 
 When you create a connection with an existing Azure storage account, you can choose between two different authentication methods:
 
 - **Credential-based**: Authenticate data access with a service principal, shared access signature (SAS) token, or account key. Users with *Reader* project permissions can access the credentials.
 - **Identity-based**: Use your Microsoft Entra ID or managed identity to authenticate data access.
 
     > [!TIP]
-    > When using an identity-based connection, Azure role-based access control (Azure RBAC) is used to determine who can access the connection. You must assign the correct Azure RBAC roles to your developers before they can use the connection. For more information, see [Scenario: Connections using Microsoft Entra ID](rbac-ai-studio.md#scenario-connections-using-microsoft-entra-id-authentication).
+    > When you use an identity-based connection, Azure role-based access control (Azure RBAC) determines who can access the connection. You must assign the correct Azure RBAC roles to your developers before they can use the connection. For more information, see [Scenario: Connections using Microsoft Entra ID](rbac-ai-studio.md#scenario-connections-using-microsoft-entra-id-authentication).
 
 
 The following table shows the supported Azure cloud-based storage services and authentication methods:
@@ -82,7 +82,7 @@ A Uniform Resource Identifier (URI) represents a storage location on your local
 
 ## Key vaults and secrets
 
-Connections allow you to securely store credentials, authenticate access, and consume data and information.  Secrets associated with connections are securely persisted in the corresponding Azure Key Vault, adhering to robust security and compliance standards. As an administrator, you can audit both shared and project-scoped connections on a hub level (link to connection rbac). 
+Connections allow you to securely store credentials, authenticate access, and consume data and information.  Secrets associated with connections are securely persisted in the corresponding Azure Key Vault, adhering to robust security and compliance standards. As an administrator, you can audit both shared and project-scoped connections on a hub level. 
 
 Azure connections serve as key vault proxies, and interactions with connections are direct interactions with an Azure key vault. Azure AI Foundry connections store API keys securely, as secrets, in a key vault. The key vault [Azure role-based access control (Azure RBAC)](./rbac-ai-studio.md) controls access to these connection resources. A connection references the credentials from the key vault storage location for further use. You won't need to directly deal with the credentials after they're stored in the hub's key vault. You have the option to store the credentials in the YAML file. A CLI command or SDK can override them. We recommend that you avoid credential storage in a YAML file, because a security breach could lead to a credential leak.