1. Enabling Read-Only AI Data Analysis in PostgreSQL


    To enable a read-only AI data analysis setup in PostgreSQL using Pulumi, we can follow these steps:

    • Create a PostgreSQL database: We'll create a PostgreSQL database where our data resides.
    • Create a read-only role: Then we'll create a PostgreSQL role with read-only permissions.
    • Grant read-only access to the data: We'll grant select privileges to this role for the tables that AI would analyze.
    • Deploy AI analysis tooling: While Pulumi isn't meant for deploying software applications like AI tools directly, it can set up the infrastructure needed for tooling to be deployed. For instance, Pulumi can provision a virtual machine or a Kubernetes cluster where you could deploy your AI application.

    For the following example, let's assume that you want to set up a PostgreSQL database and grant read-only access to specific tables for AI analysis. We will use PostgreSQL directly as it provides more granular control necessary for setting permissions:

    1. We'll define the server and database.
    2. We'll create a user (role) with read-only permissions.
    3. We'll grant this user select access on the database's tables.

    Below is a Python program using Pulumi for creating a PostgreSQL server, a database, a read-only role, and granting it access to the database.

    import pulumi import pulumi_postgresql as postgresql # Configure the PostgreSQL provider for Pulumi provider = postgresql.Provider('provider', superuser=False) # Create a new PostgreSQL database db = postgresql.Database('ai_analysis_db', name='ai_analysis', owner='postgres', provider=provider) # Create a new role for AI data analysis - read-only access ai_analysis_role = postgresql.Role('ai_analysis_role', name='ai_analysis_role', login=True, # You can set the password using Pulumi's config or any secret management approach you prefer. password='password_to_secure', # Replace with a secure password opts=pulumi.ResourceOptions(provider=provider)) # Now, grant read-only access to the AI analysis role on all tables in the public schema. # For more granular control, you can specify individual tables instead of ALL TABLES. grant_read = postgresql.Grant('ai_analysis_grant', database=db.name, role=ai_analysis_role.name, schema='public', privilege='SELECT', object_type='table', provider=provider) # Export the database name and role name so you know what got created pulumi.export('database_name', db.name) pulumi.export('read_only_role_name', ai_analysis_role.name)

    Please note the following points in the above code:

    • We first instantiate a provider with superuser=False. This means the provider will not operate with superuser privileges.
    • We then create a database named ai_analysis.
    • We create a role named ai_analysis_role with login set to True so the role can log in, but it's read-only as it does not have write permissions.
    • We use a postgresql.Grant resource to specify that the ai_analysis_role should have SELECT privileges on all tables within the public schema of the ai_analysis database. If you have multiple schemas or specific tables that you want to grant access to, you may need to create multiple Grant resources or specify the tables explicitly.
    • We export the database name and role name to make it easier to reference them in the future.

    This approach secures your data, providing access as needed to perform AI analysis while keeping your information safe from unauthorized writes or alterations. You can deploy your AI algorithms/tools on a separate compute instance and use the ai_analysis_role credentials to ensure the algorithms only have read access to the data.