the schedule key deletion function within KMS allows you to specify a waiting period before deleting a customer master key (CMK)4. The minimum waiting period is 7 days and the maximum is 30 days5. This function prevents the CMK from being used for encryption or decryption operations during the waiting period4. The other options are either invalid or ineffective for deleting a CMK within a 24-hour timeframe.

Comprehensive Detailed Explanation with all AWS References

To achieve automatic detection of anomalies in application logs in near real time and notify the security team, the following solution is appropriate:

1. Configure Amazon EKS to Send Application Logs to Amazon CloudWatch:

Log Collection:Set up Fluent Bit or Fluentd as a DaemonSet within your EKS cluster to collect application logs and forward them to Amazon CloudWatch Logs. This setup ensures that all application logs are centralized in CloudWatch for monitoring and analysis.

Generate a Data Key:

Use theaws kms generate-data-keycommand to request a data key from AWS KMS.

The data key will include both a plaintext version and an encrypted version.

Example command:

bash

aws kms generate-data-key --key-id --key-spec AES_256

Encrypt the File:

Use the plaintext data key to encrypt the 2 KB file using standard encryption libraries or utilities (e.g., OpenSSL).

Secure the Encrypted Data Key:

Store the encrypted version of the data key alongside the encrypted file for future decryption.

Least Privilege Principle:

Ensure the EC2 instance role has the minimum necessary permissions to callkms:GenerateDataKeyandkms:Decrypt.

Testing and Validation:

Verify that the encrypted file can be successfully decrypted using the stored encrypted data key and the KMS key.

AWS KMS GenerateDataKey API

AWS KMS Best Practices

Encrypting Data with AWS KMS

Implementing S3 Object Lock in compliance mode on the primary Region and configuring S3 replication to a secondary Region ensures the immutability of S3 objects, preventing them from being deleted or altered. This setup meets the requirement of protecting critical data from permanent deletion, even by users with administrative access. The replicated objects in the secondary Region inherit the Object Lock from the primary, ensuring consistent protection across Regions and aligning with disaster recovery requirements.

"When you create an Amazon GuardDuty filter, you choose specific filter criteria, name the filter and can enable the auto-archiving of findings that the filter matches. This allows you to further tune GuardDuty to your unique environment, without degrading the ability to identify threats. With auto-archive set, all findings are still generated by GuardDuty, so you have a complete and immutable history of all suspicious activity."

To enable S3 Cross-Region Replication (CRR) for objects that are encrypted with SSE-KMS, the following steps are required:

Grant the IAM role the kms.Decrypt permission for the key in us-east-1 that encrypts source objects. This will allow the IAM role to decrypt the source objects before replicating them to the destination bucket. The kms.Decrypt permission must be granted in the key policy of the source KMS key or in an IAM policy attached to the IAM role.

Grant the IAM role the kms.Encrypt permission for the key in us-west-2 that encrypts objects that are in the destination S3 bucket. This will allow the IAM role to encrypt the replica objects with the destination KMS key before storing them in the destination bucket. The kms.Encrypt permission must be granted in the key policy of the destination KMS key or in an IAM policy attached to the IAM role.

This solution will remediate the issue of encrypted objects not being replicated to the destination bucket.

The other options are incorrect because they either do not grant the necessary permissions for CRR (A, C, D), or do not use a valid encryption method for CRR (E).

Verified References:

https://docs.aws.amazon.com/AmazonS3/latest/userguide/replication-config-for-kms-objects.html

The correct answer is A. Use IAM Access Analyzer policy generation to generate a policy that allows the CloudFormation script to run and manage the stack. Attach the policy to a new IAM role. Modify the security engineer’s IAM permissions to be able to pass the new role to CloudFormation.

According to the AWS documentation, IAM Access Analyzer is a service that helps you identify the resources in your organization and accounts, such as Amazon S3 buckets or IAM roles, that are shared with an external entity. You can also use IAM Access Analyzer to generate fine-grained policies that grant least privilege access based on access activity and access attempts.

To use IAM Access Analyzer policy generation, you need to enable IAM Access Analyzer in your account or organization. You can then use the IAM console or the AWSCLI to generate a policy for a resource based on its access activity or access attempts. You can review and edit the generated policy before applying it to the resource.

To use IAM Access Analyzer policy generation with CloudFormation, you can follow these steps:

Run the CloudFormation script in a pre-production environment and monitor its access activity or access attempts using IAM Access Analyzer.

Use IAM Access Analyzer policy generation to generate a policy that allows the CloudFormation script to run and manage the stack. The policy will include only the permissions that are necessary for the script to function.

Attach the policy to a new IAM role that has a trust relationship with CloudFormation. This will allow CloudFormation to assume the role and execute the script.

Modify the security engineer’s IAM permissions to be able to pass the new role to CloudFormation. This will allow the security engineer to launch the stack using the role.

Run the CloudFormation script in the production environment using the new role.

This solution will meet the requirements of least privilege and separation of duties, as it will limit the permissions of both CloudFormation and the security engineer to only what is needed for running and managing the stack.

Option B is incorrect because creating an IAM policy that allows ec2:* and rds:* permissions is not following the principle of least privilege, as it will grant more permissions than necessary for running and managing the stack. Moreover, modifying the security engineer’s IAM permissions to be able to assume the new role is not ensuring separation of duties, as it will allow the security engineer to bypass CloudFormation and directly access the resources.

Option C is incorrect because modifying the security engineer’s IAM permissions to be able to run the CloudFormation script is not ensuring separation of duties, as it will allow the security engineer to execute the script without using CloudFormation.

Option D is incorrect because creating an IAM policy that allows ec2:* and rds:* permissions is not following the principle of least privilege, as it will grant more permissions than necessary for running and managing the stack. Using the IAM policy simulator to confirm that the policy allows the AWS API calls that are necessary to build the stack is not sufficient, as it will not generate a fine-grained policy based on access activity or access attempts.