1. Centralized AI Secrets Storage with HashiCorp Vault


    Certainly! HashiCorp Vault is a tool for secrets management, encryption as a service, and privileged access management. It allows you to securely store and access secrets, such as tokens, passwords, certificates, and encryption keys. Vault provides a unified interface to any secret, while providing tight access control and recording a detailed audit log.

    To set up a centralized AI secrets storage using HashiCorp Vault with Pulumi, you will need to write a Pulumi program that interacts with Vault's service. You will define Vault resources like the vault.Mount, which sets up a secret backend, and the vault.generic.Secret, which allows you to write secrets to the Vault store. This set-up will also typically include configuring authentication backends with vault.AuthBackend that align with your preferred method of authentication for services accessing Vault.

    Below is a Pulumi Python program that demonstrates these concepts. The program performs the following actions:

    1. Creates a Vault namespace, which is useful for multi-tenancy or for organizing secrets in your Vault instance.
    2. Sets up a secret backend by mounting it to a specific path.
    3. Writes a secret to the secret backend.
    4. Configures an authentication backend.

    What is important to bear in mind while writing this program is that you need to have a running instance of HashiCorp Vault and the proper permissions to interact with it. You'll need to configure the Pulumi Vault provider with the address and token to access your Vault instance.

    Here's the Pulumi program:

    import pulumi # Import the Pulumi Vault package import pulumi_vault as vault # Create a new Vault namespace. Namespaces are a way to create isolated environments. namespace = vault.Namespace("ai-secrets-namespace", # The `path` is where the namespace will be accessible. path="ai-secrets") # Mount a new secret backend on the specified `path`. # A mount is like a logical database that stores the secrets. secret_backend = vault.Mount("secrets-mount", # This `path` is where you'll write and read your secrets from. path="ai-secrets", # The `type` of secret backend. There are different types, such as "kv" or "aws". type="kv", # Version 2 of the key-value store provides versioning and soft delete features. options={"version": "2"}, # Link this backed to the namespace. namespace=namespace.path) # Write a secret containing some configuration data. # This data might be an API key or a database connection string, for example. secrets = vault.generic.Secret("ai-config", # The `path` should match the mounted backend's path, plus the desired path to access this specific secret. path=secret_backend.path.apply(lambda p: f"{p}/config"), # `dataJson` is the actual secret data in serialized JSON format. # Here you'll store an API key. In a real scenario, you must secure this with encryption or secret management tools. data_json='{"api_key": "supersecretvalue"}', # Define the namespace to write the secret to. namespace=namespace.path) # Set up an authentication backend. # This example uses a token auth backend, which is useful for machines or scripts. auth_backend = vault.AuthBackend("token-auth", # Specifies the type of the auth backend. type="token", # The `path` at which the auth backend will be accessible. path="token", # Link this auth backend to the namespace. namespace=namespace.path) # Using `pulumi.export` to output the paths to the secret backend and auth backend for reference. pulumi.export("namespace_path", namespace.path) pulumi.export("secrets_path", secret_backend.path) pulumi.export("auth_backend_path", auth_backend.path)

    In this program, you:

    • Define a Vault namespace (vault.Namespace) to scope where your specific secrets for AI will be managed within Vault.
    • Mount a secret backend (vault.Mount) as kv type to your namespace, specifying you're using version 2 of key-value store.
    • Write an AI service configuration secret (vault.generic.Secret) to the backend. Here, replace {"api_key": "supersecretvalue"} with your actual secret content.
    • Create a token authentication backend (vault.AuthBackend) to allow services to authenticate with Vault using a token.

    Remember to replace the data_json content with the actual secrets you wish to store and manage through Vault.

    To apply this program, simply run pulumi up, which will deploy these resources to your Vault instance as specified. Make sure you have the Pulumi CLI installed and authenticated with your Vault server credentials.

    Lastly, always ensure to handle your Vault secrets securely, following best practices for secrets management, and avoid hardcoding sensitive data into your version control systems. Use environment variables or the Pulumi configuration system to inject sensitive data at runtime.