1. Compliance Monitoring of AI Resources with AWS Config.


    AWS Config is a service that enables you to assess, audit, and evaluate the configurations of your AWS resources. This is particularly useful when dealing with AI resource setups where you need to ensure that the configurations comply with certain standards or best practices.

    For our Pulumi program, we'll create an AWS Config Rule, which is a type of resource provided by AWS to evaluate the configuration settings of your AWS resources against desired configurations. Here is how we will proceed:

    1. Create an AWS Config Configuration Recorder to capture the current and any future configurations of supported resources in your AWS account.
    2. Define an AWS Config Rule that specifies the desired configuration settings.
    3. Optionally, you can set up a Remediation Configuration if you have certain actions that should be automatically taken if a resource is not compliant.
    4. Create an AWS Config Delivery Channel to specify where AWS Config will deliver the configuration snapshots.

    In the following program, we will set up an AWS Config Rule in Pulumi using Python to monitor compliance for AI resources. Remember to replace source_identifier with the appropriate AWS managed rule identifier for AI resources or use a custom Lambda function if you have specific compliance checks.

    import pulumi import pulumi_aws as aws # Create a new configuration recorder to record the resource configurations config_recorder = aws.cfg.Recorder("config-recorder", role_arn=pulumi.Output.secret("arn:aws:iam::123456789012:role/aws-config-role"), # Replace with your IAM role ARN recording_group=aws.cfg.RecorderRecordingGroupArgs( all_supported=True, include_global_resource_types=True, )) # Create a new rule for compliance monitoring config_rule = aws.cfg.Rule("config-rule", source=aws.cfg.RuleSourceArgs( owner="AWS", # Replace 'desired-config-rule' with a valid AWS Config managed rule name, or set up a custom Lambda function. source_identifier="desired-config-rule", ), # Add any necessary input parameters for the rule (if required) input_parameters='{\"parameter\":\"value\"}') # Create a new delivery channel to deliver the configuration snapshots delivery_channel = aws.cfg.DeliveryChannel("delivery-channel", s3_bucket_name="my-config-bucket", # Replace with the name of your S3 bucket # The following is optional and used if you want to specify an S3 Key Prefix or KMS key for encryption # s3_key_prefix="config", # s3_kms_key_arn="arn:aws:kms:region:account-id:key/key-id" ) # Link the delivery channel with the configuration recorder start_configuration_recorder = aws.cfg.RecorderStatus("start-configuration-recorder", is_enabled=True, name=config_recorder.name) # Export the URL of AWS Config dashboard for easy access (use your region) pulumi.export("aws_config_console_url", pulumi.Output.concat("https://console.aws.amazon.com/config/home?region=", aws.config.region))

    This Pulumi program sets up compliance monitoring using AWS Config in the following steps:

    • Defines a Configuration Recorder to track changes to resource configurations.
    • Sets up a Config Rule to define the compliance criteria.
    • Establishes a Delivery Channel to specify how AWS Config delivers the recorded data.

    It's important to note that you'll need the appropriate permissions setup via IAM to allow AWS Config to access your resources and the S3 bucket. Ensure the role (aws-config-role) and the S3 bucket (my-config-bucket) mentioned in the code exist and are configured correctly. If needed, replace 'desired-config-rule' with a managed rule suitable for AI resources or specify the ARN of your custom Lambda function. Depending on what AI resources you need to monitor, you might also need to adjust the input parameters for the config rule and specify the S3 bucket configurations.

    After you run the Pulumi program, you can go to the AWS Management Console using the exported URL to see the status of your resources and whether they comply with the specified rule. If they don't, you can take necessary actions as specified in your remediation configurations or AWS Config Rules.