Bigquery Dataset Table Security Governance Architecture

What This Architecture Is

This architecture leverages BigQuery authorized views, Row-Level Access Policies (RLS), and resource-level labeling to enforce fine-grained access control, least-privilege security, and metadata classification across datasets and tables.

How It Meets the Requirements

• Secure data sharing with analysts: Creating an authorized view in a separate dataset allows regional business analysts to query aggregated data without granting them any IAM permissions or read access on the underlying source dataset or base tables.
• Row-level security enforcement: Creating row-level access policies with the CREATE ROW ACCESS POLICY DDL statement dynamically restricts rows returned to specified grantees based on filter conditions. BigQuery automatically assigns the system-managed bigquery.filteredDataViewer role to policy grantees. Engineers only require standard table read access (bigquery.tables.getData or roles/bigquery.dataViewer) to execute queries against the filtered table.
• Metadata and billing attribution: Because dataset labels are not inherited by underlying tables or views, applying key-value classification labels explicitly to both datasets and tables ensures accurate resource tracking and storage chargeback.

Key Advantages and Capabilities

• Decoupled data access: Authorized views permit granular access delegation while keeping raw underlying tables fully protected from broader analyst groups.
• Automated role management: Row-level policies automatically manage the filtered data viewer role for assigned principals without risk of manual IAM misconfigurations.
• Fine-grained cost attribution: Labeling resources independently at dataset and table levels enables granular billing breakdowns by cost center and sensitivity rating.
• Least-privilege adherence: Prevents over-permissioning by avoiding basic project-level roles in favor of resource-scoped predefined IAM roles.

Why This Is Optimal

This solution follows Google Cloud security best practices by strictly separating compute and data consumption via authorized views, relying on native SQL DDL for declarative row access controls, and avoiding assumptions about label inheritance.