1. Encrypting AI Data Stores with Vault Transit Secrets Engine


    To encrypt data stores for an AI application using HashiCorp Vault's Transit Secrets Engine, you would typically follow these steps:

    1. Set up Vault: You'd first need to have HashiCorp Vault installed and configured. Vault serves as a centralized location to manage secrets and protect sensitive data.

    2. Enable the Transit Secrets Engine: This engine handles cryptographic functions on data in-transit. Vault does not store the data sent to the transit secrets engine, it only returns encrypted or decrypted data.

    3. Create Encryption Keys: After enabling the Transit Secrets engine, you’ll need to create encryption keys that will be used to encrypt and decrypt your data.

    4. Encrypt Data: Before storing data in your data store, you call the Vault API to encrypt the data. Vault will return the encrypted data which you can safely store.

    5. Decrypt Data: When reading from the data store, you take the encrypted data and call the Vault API again to decrypt it.

    Here's a Pulumi program in Python which demonstrates these steps (barring the actual installation and running of Vault, which must be done separately):

    import pulumi import pulumi_vault as vault # Assuming Vault is already installed and configured # Step 2: Enable the Transit Secrets Engine transit_secret_backend = vault.TransitSecretBackend("transit-backend") # Step 3: Create an encryption key to be used with the transit secrets engine. # This key will be used for encrypting and decrypting data. encryption_key = vault.TransitSecretBackendKey("encryption-key", backend=transit_secret_backend.path, type="aes256-gcm96") # Specify the type of encryption to use. # Now let’s assume you have data that you want to encrypt before storing it in your AI data store. # Step 4: Encrypt the data # For demonstration purposes, let's say we're storing an API key. api_key_to_encrypt = "supersecretapikey" # We're simulating a call to the Vault API here. In actual code, you'd call the API and get the ciphertext. encrypted_api_key = encryption_key.id.apply( lambda key_id: vault.transit_encrypt(key_id=key_id, plaintext=api_key_to_encrypt, backend=transit_secret_backend.path) ) # Step 5: Decrypt the data when it needs to be used. # Assuming we're getting the encrypted data from our datastore and we need the plaintext. # In actual code, you'd call the API and get the plaintext back. decrypted_api_key = encryption_key.id.apply( lambda key_id: vault.transit_decrypt(key_id=key_id, ciphertext=encrypted_api_key, backend=transit_secret_backend.path) ) # Output the encrypted and decrypted values for demonstration. pulumi.export('encrypted_api_key', encrypted_api_key) pulumi.export('decrypted_api_key', decrypted_api_key)

    This program sets up the transit secrets engine and creates an encryption key. It then simulates encrypting and decrypting an API key. In a real-world scenario, you'll replace the simulation part with actual calls to the Vault API to encrypt and decrypt the sensitive data you're working with.

    Remember, when making calls to the Vault API in a production application, handle secrets responsibly, and ensure that your Vault tokens and policies are securely managed.

    Also, keep in mind that this example doesn't encrypt actual data stored in an AI data store. Instead, it just illustrates the workflow of interacting with Vault's Transit Secrets Engine using Pulumi. In a real-world application, you'd have to handle the encrypted data properly and integrate it with your AI systems and data storage solutions.