API Credential Distribution for AI Services.

Question

Pulumi · Accepted Answer

When setting up infrastructure for AI services on the cloud, you typically need to manage access to various AI APIs provided by cloud providers like AWS, Azure, or Google Cloud. These APIs often require API credentials to authenticate and authorize requests.

API Credential distribution often involves securely storing the credentials and providing them to the service that requires them without exposing these sensitive details. In cloud infrastructure, this can be done using services like AWS Secrets Manager, Azure Key Vault, or Google Cloud Secret Manager. These services allow you to store, manage, and access secrets, such as API keys, passwords, or certificates, securely.

Here's how you could use Pulumi to create a secure method for distributing API credentials for AI services using AWS as an example. In this program, I will use AWS Secrets Manager to create a new secret that could store API credentials. Once the secret is created, permissions are assigned to an IAM role which an AI service could assume to access the credentials securely.

Below is the Python Pulumi program that accomplishes this:

```python
import pulumi
import pulumi_aws as aws

# Create a secret in AWS Secrets Manager to hold the API credentials
api_credentials_secret = aws.secretsmanager.Secret(
    "apiCredentialsSecret",
    description="API credentials for AI service",
)

# Optionally, you can initialize the secret with JSON-formatted credentials.
# Replace 'YOUR_API_KEY' and 'YOUR_API_SECRET' with actual credentials.
api_credentials_value = aws.secretsmanager.SecretVersion(
    "apiCredentialsValue",
    secret_id=api_credentials_secret.id,
    secret_string='{"apiKey": "YOUR_API_KEY", "apiSecret": "YOUR_API_SECRET"}',
)

# Create an IAM role that your AI service can assume to access the secret
ai_service_role = aws.iam.Role(
    "aiServiceRole",
    assume_role_policy="""{
        "Version": "2012-10-17",
        "Statement": [
            {
                "Effect": "Allow",
                "Principal": {
                    "Service": "your-ai-service.amazonaws.com"
                },
                "Action": "sts:AssumeRole"
            }
        ]
    }"""
)

# Define a policy that allows reading the secret from AWS Secrets Manager
secret_access_policy = aws.iam.Policy(
    "secretAccessPolicy",
    policy=pulumi.Output.all(api_credentials_secret.arn).apply(lambda arn: f"""{{
        "Version": "2012-10-17",
        "Statement": [
            {{
                "Effect": "Allow",
                "Action": "secretsmanager:GetSecretValue",
                "Resource": "{arn}"
            }}
        ]
    }}""")
)

# Attach the policy to the role
secret_access_policy_attachment = aws.iam.PolicyAttachment(
    "secretAccessPolicyAttachment",
    roles=[ai_service_role.name],
    policy_arn=secret_access_policy.arn,
)

# Export the ARN of the secret and the name of the IAM role
pulumi.export("api_credentials_secret_arn", api_credentials_secret.arn)
pulumi.export("ai_service_role_name", ai_service_role.name)
```

In the above program, the `aws.secretsmanager.Secret` resource is used to create a new secret that will store your API credentials. You then create a secret version with `aws.secretsmanager.SecretVersion` that populates the secret with the actual API credentials. Be cautious to store real credentials securely and never expose them in your source code.

Next, you create an IAM role `aws.iam.Role` with a trust policy allowing a specified AI service to assume this role. The trust policy is defined in JSON format and identifies the AI service that can assume the role.

Afterward, you create an IAM policy `aws.iam.Policy` that specifically grants permission to read the secret value. This policy is then attached to the role created with `aws.iam.PolicyAttachment`.

Finally, you export the ARN (Amazon Resource Name) of the secret and the name of the IAM role so that they can be referenced elsewhere if needed.

The AI service that needs to access the API credentials would assume the `ai_service_role`, providing it access to the secret stored in AWS Secrets Manager. This setup ensures that the API credentials are securely managed and distributed.