Storing Encryption Keys for AI Data in Secret Manager

Question

Pulumi · Accepted Answer

When dealing with sensitive data, such as encryption keys for AI data, it's important to store them securely. Secret managers are designed to handle this type of sensitive information, providing a secure storage system that keeps your secrets safe while allowing fine-grained access control.

In the context of cloud infrastructure, most cloud providers offer a managed service specifically for handling secrets:

- AWS has AWS Secrets Manager.
- Google Cloud has Secret Manager.
- Azure has Azure Key Vault.

Since the prompt does not specify a cloud provider, I'll demonstrate using AWS Secrets Manager by default, which is a fully managed service to store, manage, and retrieve secrets.

The following Python program for Pulumi will create a secret in AWS Secrets Manager to store an encryption key. The secret will be encrypted with the default encryption service provided by AWS Secrets Manager, but you also have the option to use a custom customer-managed AWS KMS Key.

To work with AWS resources in Pulumi, the `pulumi_aws` package will be utilized. Here's a step-by-step program to create a secret for storing an encryption key:

```python
import pulumi
import pulumi_aws as aws

# Create a new secret in AWS Secrets Manager
# This secret can contain the encryption key you will use for your AI data.
# Replace `your-secret-encryption-key-information` with your real encryption key content.
encryption_key_secret = aws.secretsmanager.Secret("EncryptionKeySecret",
    description="Secret to store the encryption key for AI data")

# Create a new version of the secret with the specified payload.
# This payload is the actual content of the secret, in this case, the encryption key.
# Note that you should replace `your-actual-encryption-key` with the encryption key you wish to store.
encryption_key_secret_version = aws.secretsmanager.SecretVersion("EncryptionKeySecretVersion",
    secret_id=encryption_key_secret.id,
    secret_string="your-actual-encryption-key")

# To retrieve the secret in your application, you will need the ARN of the secret.
# Pulumi allows you to export output variables which can be accessed from the Pulumi CLI.
# In a production setting, you'd programmatically retrieve the secret value in your application code.
pulumi.export("encryption_key_secret_arn", encryption_key_secret.arn)

# Additionally, if you want to use the encryption key within your infrastructure,
# you might also export the value. However, beware this will expose your secret
# in plaintext in the Pulumi state file, so use it with caution.
pulumi.export("encryption_key_secret_value", encryption_key_secret_version.secret_string)
```

### How it works:

1. We import Pulumi and the AWS Pulumi package.
2. We then declare a `Secret` resource. This is an AWS Secrets Manager secret. In the `description`, you can provide any string that helps you identify the secret's purpose.
3. Next, we create a `SecretVersion` resource. This allows us to specify the content of the secret, which in this case, is our encryption key. Remember to replace `your-actual-encryption-key` with your real key.
4. Finally, we export the ARN (Amazon Resource Name) of the secret so it can be used to reference the secret in other parts of our infrastructure. Optionally, we export the secret value, but we'd typically avoid doing so for sensitive information since the Pulumi state is accessible to users of your Pulumi account.

Remember to replace placeholder values with actual encryption keys and be mindful of the security implications when handling such sensitive information.

You'll need to have the AWS CLI configured with the appropriate permissions to create these resources, and Pulumi set up to manage your AWS resources.

To run this Pulumi program, save it to a file (e.g., `main.py`), ensure you're logged in to Pulumi (`pulumi login`), and have the appropriate cloud configuration set for AWS. Then, you can run `pulumi up` to provision the resources described in your program.