1. Encrypting API Keys for AI Services Access in Databricks


    When using AI services within Databricks, it's crucial to maintain security and privacy, especially with sensitive data like API keys. A common practice is to encrypt these keys to prevent unauthorized access. In Pulumi, you can utilize the Databricks provider to achieve this by storing API keys securely as Databricks secrets.

    Here is how you can create a Databricks secret scope and then store an encrypted API key within it. Note that before running this program, you should have the Pulumi CLI and Databricks provider configured for your environment.

    First, we will use the databricks.SecretScope resource to create a secure secret scope, and then we will use the databricks.Secret resource to store an API key inside the scope. We consider the API key as a sensitive string, and thus, we will store it as a secret. Pulumi handles sensitive data securely by encrypting it in the state file.

    The databricks.SecretScope resource is a place to store the secrets, like a namespace for related secrets. The databricks.Secret resource represents a single secret key-value pair, where the key is the name you give to the secret, and the value is the encrypted content, such as your API keys.

    Below is a Pulumi program written in Python that creates a secret scope and a secret within that scope:

    import pulumi import pulumi_databricks as databricks # Create a Databricks secret scope secret_scope = databricks.SecretScope("ai-services-access", initial_manage_principal="users", description="Secret Scope for AI Services API Keys" ) # API Key that needs to be encrypted and stored as a databricks secret. api_key_to_encrypt = "your_api_key_here" # Replace with your actual API key # Create a Databricks secret for storing the API key securely. api_key_secret = databricks.Secret("ai-api-key", string_value=api_key_to_encrypt, scope=secret_scope.name, key="ai-service-api-key" # The reference key for the secret ) # Export the secret scope name and secret key name for reference. pulumi.export("secret_scope_name", secret_scope.name) pulumi.export("secret_key_name", api_key_secret.key)

    In this program:

    • We begin by importing the necessary modules, pulumi and pulumi_databricks.
    • We establish a secret scope named ai-services-access where initial_manage_principal="users" allows all users to manage the scope, and you can modify this according to your access control requirements.
    • We define the API key as a variable api_key_to_encrypt that you should replace with your actual API key.
    • We create a databricks secret, referencing our secret scope and providing the API key.

    When you run this program with Pulumi up, it will provision these resources securely in your Databricks environment. To access the API key in your Databricks notebooks, you would reference the secret scope and secret key you set here without exposing the actual value of the API key.

    After deploying this infrastructure, when you write code that requires access to AI services, instead of hardcoding your API key, you would fetch it from the Databricks secret scope you've created, thus keeping sensitive information out of your codebase.

    Please make sure to replace your_api_key_here with your real API key in a secure manner before running this Pulumi program.