https://spin.atomicobject.com/2020/09/15/aws-s3-encrypt-existing-objects/

AWS Shield Advanced provides expanded DDoS attack protection for your Amazon EC2 instances, Elastic Load Balancing load balancers, CloudFront distributions, Route 53 hosted zones, and AWS Global Accelerator standard accelerators.
AWS WAF is a web application firewall that lets you monitor the HTTP and HTTPS requests that are forwarded to your protected web application resources. You can protect the following resource types:
Amazon CloudFront distribution
Amazon API Gateway REST API
Application Load Balancer
AWS AppSync GraphQL API
Amazon Cognito user pool
https://docs.aws.amazon.com/waf/latest/developerguide/what-is-aws-waf.html

https://aws.amazon.com/cn/blogs/security/how-to-connect-to-aws-secrets-manager-service-within-a-virtual-private-cloud/
https://aws.amazon.com/blogs/security/rotate-amazon-rds-database-credentials-automatically-with-aws-secrets-manager/

This option is the most efficient because it uses data filters, which are specifications that restrict access to certain data in query results and engines integrated with Lake Formation1. Data filters can be used to implement row-level security and cell-level security, which are techniques to prevent access to portions of the data that contain sensitive information2. Data filters can be applied when granting Lake Formation permissions on a Data Catalog table, and can use PartiQL expressions to filter data based on conditions3. This solution meets the requirement of providing a secure solution to prevent access to portions of the data that contain sensitive information. Option A is less efficient because it uses an IAM role that includes permissions to access Lake Formation tables, which is a way to grant access to data in Lake Formation using IAM policies4. However, this does not provide a way to prevent access to portions of the data that contain sensitive information. Option C is less efficient because it uses an AWS Lambda function that removes sensitive information before Lake Formation ingests the data, which is a way to perform data cleansing or transformation using serverless functions. However, this could involve significant changes to the application code and logic, and could also result in data loss or inconsistency. Option D is less efficient because it uses an AWS Lambda function that periodically queries and removes sensitive information from Lake Formation tables, which is a way to perform data cleansing or transformation using serverless functions. However, this could involve significant changes to the application code and logic, and could also result in data loss or inconsistency.