1. Vault Token Auth for Controlled Access to AI Training Data


    To set up Vault Token Authentication for controlled access to AI training data, we'll use HashiCorp's Vault, which is a tool for secrets management, encryption as a service, and privileged access management. Vault handles leasing, key revocation, key rolling, and auditing.

    The following Pulumi program demonstrates how you could set up a Vault server and configure a token auth method with policies that grant specific access to training data. The data itself could be stored in an object storage service like AWS S3.

    The program will:

    1. Set up a Vault server using a Docker container for simplicity.
    2. Enable the token authentication method in Vault.
    3. Create a policy that grants read access to a specific path in the storage where the AI training data will reside.
    4. Create a token associated with the policy that will be used by applications or users to authenticate against Vault and gain access to the training data.

    Keep in mind, that in a real-world scenario, you'll want to deploy Vault in a more secure and resilient manner, possibly across multiple nodes for high availability, managed through your cloud provider's orchestration services like Kubernetes. For persistent storage, you’d also use a durable backend like AWS S3, GCP GCS, or Azure Blob Storage, and not the local storage used in this sample.

    import pulumi import pulumi_docker as docker # Start a Vault server using Docker for demonstration purposes. # In a production environment, ensure that you are running Vault with # proper persistence and security. vault_container = docker.Container("vault", image="vault:1.6.0", ports=[{ "internal": 8200, "external": 8200 }], envs=[ "VAULT_DEV_ROOT_TOKEN_ID=myroot", "VAULT_DEV_LISTEN_ADDRESS=", "VAULT_ADDR=" ]) # Vault client config - we're using the dev root token to authenticate. # In a real-world case, you'd have auth methods like k8s auth, aws auth, etc. vault_address = pulumi.Output.concat("http://", vault_container.ports.apply(lambda ports: ports[0]["external"])) vault_token = "myroot" # The Pulumi Config object doesn't handle secrets for this particualr Vault example since it's using a hardcoded dev token. # Instantiate a Vault client with the address and token. vault = VaultClient(address=vault_address, token=vault_token) # Enable a token auth method. token_auth = vault.enable_auth_method("token") # Create a policy that permits access to 'secret/data/training' which is where # we'll presume our AI training dataset is stored. training_data_policy = vault.Policy("training-data-policy", policy=f''' path "secret/data/training" { capabilities = ["read"] } ''' ) # Create a token that has the 'training-data-policy' attached. # Applications or users who present this token will inherit permissions # granted by the policy. training_token = vault.create_token("training-token", policies=["training-data-policy"], ttl="24h", # Token is valid for 24 hours. renewable=True ) # Output the token so it can be used by applications/users. # DO NOT log or expose production tokens. This is just for demonstration purposes. pulumi.export("training_token", training_token.id)

    Pulumi doesn’t have first-class support for Vault just yet, so the specifics of interacting with Vault's API—such as enabling auth methods, policies, and creating tokens—will depend on how you choose to integrate with Vault. You could use a custom provider, the Pulumi command provider, or even the Terraform Bridge depending on your requirements.

    In this program, VaultClient, vault.enable_auth_method, vault.Policy, and vault.create_token are placeholders representing how you might interact with Vault's API. You would replace these with calls to an appropriate client library for Vault in your actual code. If you're using Python, HashiCorp provides the hvac library, which you could use to interact with Vault.

    Remember to always handle secrets, such as Vault tokens, with care by using Pulumi's secrets management and never exposing them in plaintext.