AI Model Secrets Management with Vault KV v2

Question

Pulumi · Accepted Answer

To manage secrets for an AI model, we need a secure way to store and access these secrets. Vault, particularly the Key/Value version 2 (KV v2) secrets engine provided by HashiCorp, is an excellent tool for this purpose. It allows you to store, version, and manage the lifecycle of secrets.

In Pulumi, you can integrate Vault with your infrastructure as code. The `vault.kv.SecretV2` resource allows you to manage secrets in a Vault KV v2 secrets engine. This includes creating new secrets, versioning, and retrieving them programmatically.

Below is a Pulumi program written in Python that demonstrates how to set up and manage a secret for an AI model using Vault's KV v2 secrets engine. Before running this code, ensure that you have the Vault server up and running and properly configured.

```python
import pulumi
import pulumi_vault as vault

# Create a new secret in the KV v2 secrets engine.
ai_model_secret = vault.kv.SecretV2("aiModelSecret",
    name="ai-model-credentials",
    mount="secret",
    custom_metadata={
        "ai-model": "Image Classifier",
        "version": "v1.2.3",
    },
    data_json=pulumi.Output.secret("""{
        "api_key": "12345",
        "username": "ai_user",
        "password": "mysecurepassword"
    }"""),
)

# Export the name of the secret. In a real-world scenario, you wouldn't export secrets in plain text.
pulumi.export("ai_model_secret_name", ai_model_secret.name)
```

In the above program, the `vault.kv.SecretV2` resource is used. Here’s a breakdown of what’s happening:

- We import the `pulumi` and `pulumi_vault` modules, which provide us the ability to declare infrastructure as code.
- We create a secret named `aiModelSecret` within the `secret` mount path of the KV v2 secrets engine on the Vault server. This secret contains confidential data like `api_key`, `username`, and `password`, formatted as JSON.
- `custom_metadata` is an optional field that allows us to provide metadata for the secret; in this case, information about the AI model and its version.
- The `data_json` argument should be a JSON string containing the key-value pairs representing the secrets. Using `pulumi.Output.secret` ensures that the value is encrypted and handled securely by Pulumi.
- The program exports the name of the secret, not the secret's content, to avoid revealing sensitive information.

Make sure to replace the `data_json` content with your actual secret values. Keep in mind that secrets management is sensitive, and the policies around access and management should be strictly followed.

To apply this program, ensure you have Pulumi and Vault installed and configured:

1. Set up your Vault server and enable the KV v2 secrets engine at the desired path (by default, it's 'secret/').
2. Write the above code into a file, e.g., `main.py`.
3. Run `pulumi up` to deploy the program, which will create or update the secret in Vault.

Remember to handle and distribute secrets responsibly. Always conform to your organization’s security policies when dealing with sensitive information.