This question requires implementing fine-grained data access control across multiple services based on the Principle of Least Privilege.
Project/Service Access (IAM): Granting project-level group permissions with specific Cloud IAM roles (e.g., BigQuery Data Viewer) is the primary way to control who has access to which project's resources.
Data Isolation (Service-Specific): To ensure only relevant data is accessed and to protect sensitive information within the datasets, you must use the most granular control mechanism available for each service:
BigQuery: Authorized Views allow access to specific query results (subsets of data) without granting access to the underlying table.
Cloud Storage: Uniform bucket-level access simplifies and tightens security by forcing all access to be controlled by IAM, preventing accidental object-level exposure.
Cloud SQL: Database Roles are the native, most granular way to control access within the database itself (e.g., read-only access to specific tables).
Extracts (Conceptual Basis):
"The principle of least privilege dictates that users should only have the permissions necessary to perform their jobs. Granular access is enforced using a combination of IAM roles and service-native access controls." (Source 5.1)
"For BigQuery, using authorized views is the standard way to limit data exposure to users who should only see a subset of data." (Source 5.2)
