Vault Auth Policies for Secure AI Model Deployment

Question

Pulumi · Accepted Answer

To securely deploy an AI model, we need to consider how to manage authentication and authorization to the underlying infrastructure and services. HashiCorp Vault is commonly used as a centralized secrets management system to handle sensitive data and control access to various resources.

Vault has multiple ways to handle authentication—such as tokens, username/password, and cloud identity—and authorization through policies which define what authenticated entities can and cannot do. In this case, we’ll discuss a Pulumi program that sets up Vault with specific policies and identities that can be used in the context of deploying and managing an AI model.

Initially, we'll set up an example with the following components:

1. A Vault Auth Backend to enable authentication methods.
2. A Vault Policy that outlines the permissions for a specific role.
3. Vault AWS Auth Backend Role which maps AWS IAM principals to Vault roles.
4. Identity groups and entity policies to manage group-specific policies.

The Vault resources that we're going to define correspond to Vault's approach to Identity and Access Management. Here's a brief explanation of each:

- **Vault Auth Backend**: This is like an authentication method in Vault. It's the way entities authenticate themselves. We might enable an AWS method to allow certain AWS instances to authenticate with Vault.

- **Vault Policy**: It's a set of rules that codify the permissions within Vault. For instance, a policy could specify that certain users can read from a specific path in the secrets engine, but not write.

- **Vault AWS Auth Backend Role**: This specific resource allows you to tie AWS principals (like EC2 instances, IAM users, etc.) to Vault policies, defining what authenticated AWS entities are allowed to do in Vault.

- **Vault Identity Group Policies and Entity Policies**: These facilitate grouping of entities (like users or machines) and assigning them policies. Entities can belong to multiple groups, and you can manage permissions by group rather than by individual entities.

For a more practical demonstration, consider this Pulumi program as a starting point:

```python
import pulumi
import pulumi_vault as vault

# Assuming you have initialized and unsealed your Vault server, and
# have the address and a token set in your environment variables.

# Enable an AWS auth backend
aws_auth_backend = vault.AuthBackend("aws-auth",
                                     path="aws",  # The path for this auth method within Vault
                                     description="Authentication for AWS users",
                                     type="aws")

# Create a Vault policy to give permissions appropriate for your AI model deployment
ai_model_policy = vault.Policy("aiModelPolicy",
                               name="ai-model-policy",
                               # Inline policies are strings defining the actual policies
                               policy="""
                               path "secret/data/ai/*" {
                                   capabilities = ["create", "read", "update"]
                               }
                               """
                               )

# Create an AWS auth backend role
aws_auth_backend_role = vault.aws.AuthBackendRole("ai-model-role",
                                                  backend=aws_auth_backend.path,
                                                  role="ai-model-ec2-role",
                                                  auth_type="ec2",
                                                  policies=[ai_model_policy.name]
                                                  # add other configurations such as bound_ami_ids, bound_instance_ids, etc.
                                                  )

# Create an entity to represent an application or instance responsible for AI model
entity = vault.identity.Entity("ai-model-entity",
                               policies=[ai_model_policy.name],
                               metadata={
                                    "environment": "production"
                               })

# Group policies can be applied to entities based on group membership
group = vault.identity.GroupPolicies("ai-model-group",
                                    groupId=entity.id,  # Assume you have the entity ID
                                    policies=[ai_model_policy.name]
                                    )

pulumi.export("auth_backend_path", aws_auth_backend.path)
pulumi.export("ai_model_policy_name", ai_model_policy.name)
pulumi.export("entity_id", entity.id)
```

In the above program:

- We set up an AWS auth backend at the given path inside Vault and named it.
- We created a Vault policy named `ai-model-policy` which allows creating, reading, and updating operations on all secrets paths starting with `secret/data/ai/`. These paths should store the secrets necessary for your AI model operations.
- Then, we defined an AWS backend role and tied it to our ai-model-policy. Any AWS entity that matches this role (by meeting the role's conditions, which could be an EC2 instance tag, IAM role ARN, etc.) could then authenticate using this backend and receive the permissions outlined by the policy.
- An entity is defined, representing the application or AI model within the Vault. This entity is tied to the `ai-model-policy` policy directly.
- Finally, we establish a group which links to our entity and assign it the `ai-model-policy`.

This is a simplified configuration. Vault offers rich fine-grained controls that can be tweaked based on your specific security requirements. Always refer to the official [Vault AWS Auth Backend documentation](https://www.pulumi.com/registry/packages/vault/api-docs/aws/authbackendrole/) and [Vault Policy documentation](https://www.pulumi.com/registry/packages/vault/api-docs/policy/) to understand all available options and ensure you're achieving the desired level of security.