A. Store the data in a single Amazon S3 bucket. Create an IAM role for every combination of job type and business unit that allows for appropriate read/write access based on object prefixes in the S3 bucket. The roles should have trust policies that allow the business unit’s AWS accounts to assume their roles. Use IAM in each business unit’s AWS account to prevent them from assuming roles for a different job type. Users get credentials to access the data by using AssumeRole from their business unit’s AWS account. Users can then use those credentials with an S3 client.
B. Store the data in a single Amazon S3 bucket. Write a bucket policy that uses conditions to grant read and write access where appropriate, based on each user’s business unit and job type. Determine the business unit with the AWS account accessing the bucket and the job type with a prefix in the IAM user’s name. Users can access data by using IAM credentials from their business unit’s AWS account with an S3 client.
C. Store the data in a series of Amazon S3 buckets. Create an application running in Amazon EC2 that is integrated with the company’s identity provider (IdP) that authenticates users and allows them to download or upload data through the application. The application uses the business unit and job type information in the IdP to control what users can upload and download through the application. The users can access the data through the application’s API.
D. Store the data in a series of Amazon S3 buckets. Create an AWS STS token vending machine that is integrated with the company’s identity provider (IdP). When a user logs in, have the token vending machine attach an IAM policy that assumes the role that limits the user’s access and/or upload only the data the user is authorized to access. Users can get credentials by authenticating to the token vending machine’s website or API and then use those credentials with an S3 client.