1. Secure Deployment of AI Services Across Accounts


    Deploying AI services securely across cloud accounts involves configuring the services to ensure they are accessible only to authorized entities and integrating them with your cloud provider's security mechanisms. In the context of Microsoft Azure, securely deploying services like Azure Cognitive Services or Azure Machine Learning can be done using the available Pulumi resources that allow you to specify networking and access policies, manage identities, and scale the deployments securely.

    Below is a Pulumi program written in Python that demonstrates how to deploy an Azure Cognitive Services account securely across different accounts. This program uses the azure-native.cognitiveservices.Account Pulumi resource, which allows you to create and manage an Azure Cognitive Services account with specified properties like network restrictions and identity management.

    Here are the key points we will cover in our secure deployment:

    1. Resource Group: Resources in Azure are grouped into resource groups, which provide a way to monitor, control access, provision, and manage billing for collections of assets that are needed to be managed together.

    2. Cognitive Services Account: We will create a Cognitive Services account with specific properties like the SKU, kind, network rules, and identity configurations. We will enable network security by specifying IP rules and indicating that only the listed IPs can access the service.

    3. Managed Identity: We will also create a managed identity (either System Assigned or User Assigned) to securely access other Azure services without the need for credentials in the code.

    4. Pulumi Exports: Export outputs like the endpoint and primary key so they can be used outside of Pulumi, such as in CI/CD or integration with other services.

    import pulumi import pulumi_azure_native as azure_native # Create a resource group for organizing resources in the Azure portal. resource_group = azure_native.resources.ResourceGroup("aiResourceGroup") # Create an Azure Cognitive Services Account with secure networking and managed identity settings. cognitive_services_account = azure_native.cognitiveservices.Account( "aiCognitiveServicesAccount", resource_group_name=resource_group.name, kind="CognitiveServices", # The type of account. CognitiveServices includes multiple AI services. sku=azure_native.cognitiveservices.SkuArgs( name="S0", # The pricing tier for the account. S0 is a standard tier. tier="Standard" ), # Set up network rules to allow traffic only from specific IP addresses. properties=azure_native.cognitiveservices.AccountPropertiesArgs( public_network_access="Enabled", network_acls=azure_native.cognitiveservices.NetworkRuleSetArgs( default_action="Deny", # Set default action to deny to ensure the list is exclusive. ip_rules=[ azure_native.cognitiveservices.IpRuleArgs(value=""), # Replace with the actual IP address. #... Add more IPs as needed. ] ) ), # Assign a system-assigned managed identity to the Cognitive Services account for secure interactions with other Azure services. identity=azure_native.cognitiveservices.IdentityArgs( type="SystemAssigned" ), location="eastus" # Specify the Azure region to deploy the resource. ) # Export the Cognitive Services Account's endpoint and primary key for use outside of Pulumi. pulumi.export("endpoint", cognitive_services_account.endpoint) pulumi.export("primary_master_key", cognitive_services_account.primary_master_key)

    This program starts by importing the necessary Azure libraries. It creates an Azure resource group to house our Cognitive Services account. After that, it sets up a Cognitive Services account with restricted access to predefined IP addresses and assigns a system-managed identity for secure, credential-less access to other Azure services.

    Upon running this program with Pulumi, it provisions the resources in the Azure cloud and outputs the Cognitive Services account's endpoint and primary key. These outputs can be used to configure client applications that consume Azure AI services in a secure way.

    Please note that this is a simplified scenario. In production, you would have much more complex network configurations, may need to bind it to private networks (e.g., VNET integration), and ensure monitoring and compliance with your organization's guidelines. You would also need to manage and rotate keys securely with Azure Key Vault or integrate with Azure Active Directory for more complex identity and access management.