1. Granular Permissions for AI Data Scientists using AWS IAM


    Creating granular permissions for AI Data Scientists in AWS involves defining specific access controls which align closely with their roles and responsibilities. In AWS, the Identity and Access Management (IAM) service is used to create and manage these permissions. Some of the key components in IAM that you will use include roles, policies, and groups.

    Roles are IAM entities that define a set of permissions for making AWS service requests. Roles do not have standard long-term credentials such as passwords or access keys associated with them. Instead, if a user assumes a role, temporary security credentials are created dynamically and provided to the user.

    Policies are documents that define permissions and can be attached to IAM identities or resources. Policies can be managed by AWS (AWS Managed Policies) or created by users (Customer Managed Policies).

    Groups are collections of IAM users where policies can be attached to a group, and all users in that group will inherit those permissions.

    Below, I'll provide you with a basic Pulumi program in Python that creates roles, policies, and groups, with the assumption that you're looking to equip your AI Data Scientists with permissions to perform specific tasks like accessing certain S3 buckets, running machine learning models, or analyzing data with AWS services like SageMaker.

    First, you need to ensure you have installed the required packages by Pulumi for AWS:

    pip install pulumi pulumi_aws

    Let's get started with the Pulumi program:

    import pulumi import pulumi_aws as aws # Define a customer managed policy for the AI Data Scientists data_scientists_policy_document = aws.iam.get_policy_document( statements=[ aws.iam.GetPolicyDocumentStatementArgs( actions=[ "sagemaker:*", # Allowing all actions on SageMaker. "s3:GetObject", # Allow reading specific S3 buckets. "s3:ListBucket", # Allow listing S3 buckets. ], resources=[ "arn:aws:sagemaker:*:*:notebook-instance/*", # Replace with specific ARNs. "arn:aws:s3:::ai-datasets", # Replace with specific S3 buckets. "arn:aws:s3:::ai-datasets/*", # Note you can further restrict paths. ], ), ], ) ai_data_scientists_policy = aws.iam.Policy("aiDataScientistsPolicy", description="A policy for AI data scientists", policy=data_scientists_policy_document.json, ) # Create an IAM role for AI Data Scientists ai_data_scientists_role = aws.iam.Role("aiDataScientistsRole", assume_role_policy=aws.iam.assume_role_policy_for_principal( principal={"Service": "sagemaker.amazonaws.com"}, ).json, ) # Attach the policy to the role ai_data_scientists_role_policy_attachment = aws.iam.RolePolicyAttachment("aiDataScientistsRolePolicyAttachment", role=ai_data_scientists_role.name, policy_arn=ai_data_scientists_policy.arn, ) # Create an IAM group for AI Data Scientists ai_data_scientists_group = aws.iam.Group("aiDataScientistsGroup") # Attach the policy to the group ai_data_scientists_group_policy_attachment = aws.iam.GroupPolicyAttachment("aiDataScientistsGroupPolicyAttachment", group=ai_data_scientists_group.name, policy_arn=ai_data_scientists_policy.arn, ) # Exporting the ARNs of the created resources pulumi.export("policy_arn", ai_data_scientists_policy.arn) pulumi.export("role_arn", ai_data_scientists_role.arn) pulumi.export("group_name", ai_data_scientists_group.name)

    This program first defines a managed IAM policy specifically for the AI Data Scientists. The policy includes permissions for typical tasks like interfacing with SageMaker, and reading from an S3 bucket named ai-datasets.

    Next, we create an IAM role named aiDataScientistsRole and a policy that allows the SageMaker service to assume the role. After creating this role, we attach the managed policy to the role using RolePolicyAttachment.

    In a similar manner, we create an IAM group for AI Data Scientists and attach the managed policy to the group, thereby granting every user in this group the permissions defined in the policy.

    By exporting the ARNs and names of the policies and roles, you ensure that these resources can be referenced in other parts of your Pulumi stack or even in other stacks.

    To run this program you will need to set up Pulumi and configure AWS credentials. Once that's done, you can run the command pulumi up to deploy the resources defined in this program.

    The role and policies define granular permissions that cater to the operations commonly performed by AI Data Scientists, like managing machine learning models and accessing data necessary for their analyses. Remember, you can further customize the actions and resources inside get_policy_document to align the permissions closely with your data scientists' workflows.