Identity Federation for Cross-Cloud AI Services.

Question

Pulumi · Accepted Answer

Identity Federation is a system that allows users to access multiple IT systems or services using one set of credentials, which can greatly simplify access management across diverse systems in cloud environments. In a cross-cloud scenario, we aim to create a federated identity system that would enable unified authentication across different cloud service providers, which would be particularly beneficial when leveraging AI services that span across these different providers.

Below, we're going to write a Pulumi program that sets up identity federation in both Azure and Google Cloud, which are two common cloud providers that you might use to host AI services. This setup will involve creating federated identity credentials in Azure, and implementing workload identity federation in Google Cloud, allowing a service in one cloud to authenticate and interact with services in the other without requiring separate credentials.

Here's a high-level overview of what we'll accomplish in this program:

1. Configure federated identity credentials using Azure Active Directory (Azure AD), which enables our application registered in Azure AD to be recognized by Google Cloud services.
2. Set up a Google Cloud IAM Workload Identity Pool and Provider, which will allow services running on Azure to assume a Google-managed identity.

First, we'll create federated identity credentials in Azure that enable an application registered in Azure AD to be federated with a Google Cloud identity. We will use the `ApplicationFederatedIdentityCredential` resource from the `azuread` provider because it manages app federation within Azure AD. It allows an application to present tokens issued by an external identity provider to access Azure AD-secured resources.

Next, we will create a Workload Identity Pool and a Workload Identity Provider in Google Cloud. The `WorkloadIdentityPool` resource lets us set up a pool that can include Azure AD as an external identity provider, and the `WorkloadIdentityPoolProvider` resource allows us to specify the details of this integration, such as the issuer URL and attribute mapping.

Let's start with the Pulumi code to set up identity federation:

```python
import pulumi
import pulumi_azuread as azuread
import pulumi_google_native.iam.v1 as google_iam

# Step 1: Set up Federated Identity Credential in Azure Active Directory (Azure AD)

# Replace these variables with your actual application and federation information
azure_tenant_id = "your-azure-tenant-id"
azure_application_id = "your-azure-application-object-id"
issuer_url = "https://sts.windows.net/your-azure-tenant-id/"
subject = "your-subject"
audiences = ["api://AzureADTokenExchange"]

# Create the Application Federated Identity Credential in Azure AD
application_federated_credential = azuread.ApplicationFederatedIdentityCredential("app-fed-id-cred",
    application_object_id=azure_application_id,
    issuer=issuer_url,
    subject=subject,
    audiences=audiences,
    display_name="Cross-Cloud AI Services Federation"
)

# Step 2: Set up Workload Identity Federation in Google Cloud

# Replace with your GCP project
gcp_project = "your-google-cloud-project"

# Create a Workload Identity Pool in Google Cloud
workload_identity_pool = google_iam.WorkloadIdentityPool("workload-identity-pool",
    parent=f"projects/{gcp_project}/locations/global",
    displayName="Cross-Cloud AI Services Workload Identity Pool",
    disabled=False
)

# Create a Workload Identity Pool Provider in Google Cloud pointing to Azure AD
workload_identity_pool_provider = google_iam.WorkloadIdentityPoolProvider("workload-identity-pool-provider",
    parent=workload_identity_pool.name,
    workloadIdentityPoolProviderId="azure-provider",
    displayName="Azure Workload Identity Provider",
    disabled=False,
    oidc=google_iam.WorkloadIdentityPoolProviderOidcArgs(
        issuerUri=issuer_url,
        allowedAudiences=audiences
    )
)

# Export relevant information
pulumi.export("azure_application_federated_credential_id", application_federated_credential.id)
pulumi.export("google_workload_identity_pool_id", workload_identity_pool.id)
pulumi.export("google_workload_identity_pool_provider_id", workload_identity_pool_provider.id)
```

Here is a breakdown of this program:

- We begin by importing the necessary Pulumi packages for Azure Active Directory and Google IAM.
- We then set up an `ApplicationFederatedIdentityCredential` in Azure AD, which allows our Azure AD application to be recognized as an identity within Google Cloud. Make sure to fill in your own tenant ID, application object ID, and other parameters specific to your federation setup.
- In Google Cloud, we create a Workload Identity Pool, which is a container for a set of workload identity providers.
- We then define a Workload Identity Pool Provider specifically for Azure, specifying its issuer URL, the audience that it will trust, and other settings.
- Lastly, we export the identifiers of the created resources for reference and use in other parts of our infrastructure or in applications that need to integrate with this federated identity setup.

This setup allows services hosted on Azure to be authenticated on Google Cloud Platform, enabling a seamless cross-cloud AI service experience.

Remember to replace placeholder values with actual values specific to your cloud environments and to customize the audience, issuer URL, and other settings to match your federation needs. Also, please ensure that you have the appropriate permissions to create these resources in both Azure and Google Cloud.

By executing this program with Pulumi, the necessary cloud resources for identity federation across Azure and Google Cloud will be created. This setup is a foundational step to enable secure and streamlined cross-cloud AI operations.