1. Seamless Access Control for Comprehend with Service-Linked Roles


    To implement seamless access control for AWS Comprehend with service-linked roles, you would typically use AWS Identity and Access Management (IAM) to set up the roles and policies that will allow AWS Comprehend to access other AWS resources on your behalf. Service-linked roles are predefined by the service itself (in this case, AWS Comprehend) and include all the permissions that the service requires to call other AWS services on your behalf.

    Here's a program written in Python using Pulumi that sets up an AWS Comprehend resource along with an associated service-linked role. Pulumi allows you to describe your infrastructure using general programming language constructs and APIs, which it then turns into a cloud setup.

    To start, you will need to create an instance of the AWS Comprehend EntityRecognizer resource. This resource enables integration of Comprehend's entity recognition capabilities into applications. We use the aws.comprehend.EntityRecognizer from Pulumi's AWS package. It needs a name and several configurations, including input data location (S3 bucket) and the IAM role ARN required to access the data.

    The EntityRecognizer requires an IAM role with appropriate policies to access AWS Comprehend services and your S3 buckets. The program defines an IAM role and attaches policies that grant the necessary permissions. AWS Comprehend can automatically create a service-linked role the first time a service-specific action is performed, but Pulumi allows you to create and configure it as code, providing better visibility and control.

    Let's go through the setup with Pulumi and AWS in Python:

    import pulumi import pulumi_aws as aws # Define the IAM policy that allows access to S3 and AWS Comprehend. comprehend_policy_document = aws.iam.get_policy_document(statements=[ { "actions": ["comprehend:*"], "resources": ["*"] }, { "actions": ["s3:GetObject"], "resources": ["arn:aws:s3:::example-bucket/*"] } ]) # Create an IAM role with the above policy document. comprehend_role = aws.iam.Role("comprehendRole", assume_role_policy=comprehend_policy_document.json ) # Attach the AWS Comprehend service role policy to the IAM role. aws.iam.RolePolicyAttachment("comprehendServiceRolePolicy", role=comprehend_role.name, policy_arn=aws.iam.ManagedPolicy.AMAZON_COMPREHEND_SERVICE_ROLE_POLICY.arn ) # Define an EntityRecognizer with its required configurations. # "input_data_config" should point to the S3 bucket where your data is stored. # For example, you might store your training data in "s3://example-bucket/data". entity_recognizer = aws.comprehend.EntityRecognizer("entityRecognizer", recognizer_name="my-entity-recognizer", data_access_role_arn=comprehend_role.arn, input_data_config={ "data_format": "ONE_DOC_PER_FILE", "documents": { "s3_uri": "s3://example-bucket/data" } }, language_code="en" ) pulumi.export("entity_recognizer_arn", entity_recognizer.arn)

    In the above program:

    1. We create an IAM policy document that defines the permissions required by AWS Comprehend to access S3 and execute Comprehend actions.
    2. We create an IAM role and attach the Comprehend service role policy to it. This allows AWS Comprehend to assume this role.
    3. We set up an AWS Comprehend EntityRecognizer and pass the IAM role we created as part of the data_access_role_arn.
    4. Lastly, we export the ARN of the Comprehend EntityRecognizer so that you can reference it in other parts of your application or infrastructure.

    Once you've written this program in a file __main__.py, you run it using the Pulumi CLI, which will set up these resources on AWS. You'll need the Pulumi CLI and AWS CLI configured with the necessary credentials and permissions in your environment to execute this.