1. Vault for Secure AI Model Configuration Storage


    Vault by HashiCorp is a tool for securely managing secrets and protecting sensitive data within modern computing environments. It's commonly used to manage environment variables, API keys, and other secrets in cloud-native environments which can include configurations for AI models.

    For the purpose of storing AI model configurations securely, we'll leverage Vault's Key-Value (KV) secrets engine. The KV secrets engine is a simple key-value store that allows you to store and manage secrets. We will use the vault.kv.SecretV2 resource from Pulumi's Vault package to store the AI model configuration as a secret.

    Here's a Pulumi program in Python that demonstrates how to store a secret in Vault using the KV v2 secrets engine.

    1. Setting up Vault: Ordinarily, you would need a running Vault server. Pulumi won't create this server for you, but you can easily do it on any supported cloud provider or on-premises. This program assumes you have a Vault server reachable at VAULT_ADDR and your Pulumi environment has been authenticated with Vault.

    2. Creating a Secret in Vault: We'll define a secret with some example configuration data that might be used for an AI model.

    Let's go through the process of implementing the above:

    import pulumi import pulumi_vault as vault # Define a secret in Vault for storing AI model configurations # Secret data can be in the form of key-value pairs. # Replace `'api_key': '12345'` with your actual model parameters and configurations. # Ensure you have Vault set up and the Vault provider configured with appropriate credentials. ai_model_config = vault.kv.SecretV2("aiModelConfig", data_json=pulumi.Output.secret({"api_key": "12345", "model_param": "param_value"}).apply(lambda config: pulumi.Output.secret(config)), mount="secret", # This is the default path where the KV v2 secrets engine is enabled; change if you have another mount path. options=vault.kv.SecretV2OptionsArgs( cas_required=False, max_versions=10, ) ) # Export the Vault secret path pulumi.export("vault_secret_path", ai_model_config.path)

    In the above program:

    • We import pulumi and pulumi_vault, which are the Python packages for Pulumi and the Vault provider.
    • We create a SecretV2 resource which represents a secret in the Vault KV store.
    • data_json is where we write our confidential AI model configuration. This must be a JSON encoded string, hence we use pulumi.Output.secret({...}).apply(lambda config: pulumi.Output.secret(json.dumps(config))) to ensure our secret data is treated as a Pulumi secret, and the JSON encoding is correctly applied.
    • The mount parameter specifies the path where the KV secrets engine is enabled in Vault. The default is secret, but this might be different depending on your Vault setup.
    • The options parameter takes a SecretV2OptionsArgs object, which allows us to set various settings for our secret, such as requiring check-and-set (CAS) operations and limiting the number of versions stored.
    • Finally, we use pulumi.export to export the path of the secret so that you can reference this path in your applications to fetch the stored configurations.

    Please replace the placeholder configuration with actual sensitive data you wish to store securely for your AI model.

    Before running the above Pulumi program, ensure you have Vault properly set up and configured, including having the Vault server running and the necessary policies and auth methods configured to interact with the server. If you're not sure how to do this, refer to Vault's documentation for guidance.