Implementing Least Privilege Access Policies for Data Engineering Architecture on AWS (RDS Aurora MySQL, AWS Glue, Redshift, QuickSight)

Please find below a sample AWS IAM policy which can be assiciated to an AWS User/role. The policies attached would be able to address your requirement for the permission requirements.

This policy below ensures that the user would have minimum permissions to interact with each of the services: Redshift, Quicksight, RDS and Glue

{
    "Version": "2012-10-17",
    "Statement": [
      {
        "Effect": "Allow",
        "Action": [
          "rds-db:connect",
          "rds:DescribeDBInstances",
          "rds:DescribeDBClusters"
        ],
        "Resource": "arn:aws:rds:region:account-id:db:db-instance-id"
      },
      {
        "Effect": "Allow",
        "Action": [
          "glue:GetJob",
          "glue:StartJobRun",
          "glue:GetCrawler",
          "glue:StartCrawler"
        ],
        "Resource": [
          "arn:aws:glue:region:account-id:job/job-name",
          "arn:aws:glue:region:account-id:crawler/crawler-name"
        ]
      },
      {
        "Effect": "Allow",
        "Action": [
          "s3:GetObject",
          "s3:PutObject",
          "redshift:CopyFromS3",
          "rds-db:connect"
        ],
        "Resource": "*"
      },
      {
        "Effect": "Allow",
        "Action": [
          "redshift:DescribeClusters",
          "redshift:GetClusterCredentials",
          "redshift:CopyFromS3",
          "redshift:ExecuteQuery"
        ],
        "Resource": "arn:aws:redshift:region:account-id:cluster/cluster-id"
      },
      {
        "Effect": "Deny",
        "Action": [
          "redshift:ModifyCluster",
          "redshift:DeleteCluster"
        ],
        "Resource": "*"
      },
      {
        "Effect": "Allow",
        "Action": [
          "quicksight:DescribeDataSet",
          "quicksight:ListDashboards",
          "quicksight:DescribeDashboard"
        ],
        "Resource": [
          "arn:aws:quicksight:region:account-id:dataset/dataset-id",
          "arn:aws:quicksight:region:account-id:dashboard/dashboard-id"
        ]
      },
      {
        "Effect": "Deny",
        "Action": [
          "quicksight:UpdateDashboardPermissions",
          "quicksight:DeleteDashboard"
        ],
        "Resource": "*"
      }
    ]
  } 

** Please note, this policy would require further addition of policies if your instances are KMS encrypted or if you need to interact with other resources like S3.

To address your other queries:

Q1> What are the best practices for creating these least-privilege policies while ensuring the smooth operation of the ETL process and reporting?

ANS:

Use Role-Based Access Control (RBAC)

• Organize permissions based on roles within your organization. Define roles for different responsibilities (e.g., ETL developer, data analyst) and assign users to these roles. This simplifies managing permissions and ensures consistency.

Use Resource-Level Permissions

• Where possible, restrict permissions to specific resources (e.g., particular RDS databases, Glue jobs, Redshift clusters) rather than applying permissions to all resources of a given type. This minimizes the potential impact of any compromised credentials.

Enable Logging and Monitoring

• Use AWS CloudTrail to log all API calls made to AWS services. Enable Amazon CloudWatch to monitor activity and set up alarms for suspicious behavior (e.g., attempts to access restricted resources).

Q2> Are there any specific recommendations for securing access to each of these services while maintaining efficiency and functionality?

ANS:

1. RDS Aurora MySQL

• Encryption: Enable encryption for RDS Aurora MySQL to protect data at rest.
• Network Security: Use VPC security groups to control inbound and outbound traffic to the database. Ensure that only necessary services (e.g., Glue) have access.
• IAM Database Authentication: Utilize IAM roles for database authentication, eliminating the need for static database credentials.

2. AWS Glue

• Limit Glue Job Permissions: Only grant Glue jobs access to the specific S3 buckets, databases, and Redshift clusters they need. Avoid giving Glue jobs wildcard permissions like s3:* or redshift:*.
• Script Security: Store Glue job scripts in a secure S3 bucket with limited access, and use versioning to track changes.

3. Amazon Redshift

• Cluster Security Groups: Restrict access to the Redshift cluster by defining rules in the associated security groups. Only allow connections from trusted IP ranges or services like QuickSight.
• Column-Level Security: Use Redshift's built-in column-level security to restrict access to sensitive data within tables. This is particularly useful for complying with data protection regulations.

4. Amazon QuickSight

• Restrict Access to Datasets: Use QuickSight’s dataset-level permissions to control which users or groups can access specific datasets. This helps ensure that users only see the data they need.
• Use Group-Based Access: Manage QuickSight access through IAM groups, aligning with the RBAC approach. This simplifies administration and ensures that users have appropriate access.

5. Cross-Service Considerations

• Data Encryption: Ensure that data is encrypted in transit between services (e.g., from RDS Aurora MySQL to Glue, and from Glue to Redshift). Use SSL/TLS for all connections.
• Service Limits and Quotas: Be aware of AWS service limits and quotas to prevent accidental denial of service. For example, configure Glue job concurrency limits and Redshift query queue configurations to optimize performance.