1. Encrypting AI Generated Data at Rest with Vault Transit


    In order to encrypt data at rest using HashiCorp Vault's Transit secrets engine, you would typically perform the following steps:

    1. Enable the Transit Secrets Engine: This engine is responsible for cryptographic functions, such as managing encryption keys and encrypting/decrypting data.

    2. Create Encryption Keys: These keys are used to encrypt and decrypt your data.

    3. Encrypt Data: Send the data to Vault's Transit endpoint, which will use the specified keys to encrypt the data.

    4. Store Encrypted Data: The encrypted data is now considered "at rest" and can be stored safely in a database or other storage systems.

    5. Decrypt Data: When needed, the encrypted data is sent back to Vault to be decrypted using the same key.

    The following is a Pulumi Python program that demonstrates setting up the Vault Transit Secrets Engine and creating an encryption key that could be used to encrypt and decrypt AI-generated data. Please note that actual data encryption and decryption API operations are out of the scope of Pulumi and are handled directly through Vault's API or client libraries.

    import pulumi import pulumi_vault as vault # Enable the Transit Secrets Engine transit_secrets_engine = vault.Mount("transit-secrets-engine", path="transit", type="transit", description="Transit secrets engine for encryption/decryption of data") # Create an Encryption Key encryption_key = vault.transit.SecretBackendKey("encryption-key", backend=transit_secrets_engine.path, name="ai-generated-data-key", type="rsa-4096") # Export the transit engine's path and the encryption key name for reference pulumi.export("transit_secrets_engine_path", transit_secrets_engine.path) pulumi.export("encryption_key_name", encryption_key.name)


    1. We import the necessary Pulumi libraries for Vault.

    2. We use vault.Mount to enable the Transit secrets engine. This is a way to dynamically enable secret engines in Vault using Pulumi.

    3. The path attribute specifies the path where we will mount the engine. We use "transit" in this case.

    4. We then declare vault.transit.SecretBackendKey to create an encryption key that can be used for our data. The backend field links this key with the Transit secrets engine we just enabled, and name is the name for the encryption key. We're using an RSA 4096-bit key rsa-4096 for the type attribute, which is one of the supported key types in Vault Transit.

    5. Finally, we export the path of the Transit secrets engine mount and the name of the encryption key we created. These outputs can be used later on to configure applications to use the engine and key for encrypting/decrypting data.

    When using this program, replace import pulumi_vault as vault with the correct package name if you're using a different language SDK or if your SDK version names the package differently.

    To proceed with using these resources, you'll need to interact with Vault's API directly or use one of its SDKs. Pulumi's role ends at the infrastructure setup. From here, you would write code in your application to interact with the Vault API to encrypt and decrypt your AI-generated data. This typically involves using Vault client libraries or invoking the Vault API over HTTP, providing the necessary authentication and referencing the key you've created in your Pulumi program.