Bigquery Analytics Hub Iam Governance Security

What the Solution Is

This architecture leverages BigQuery Analytics Hub combined with native BigQuery security primitives—specifically authorized views, row-level security (RLS), and column-level policy tags with dynamic data masking—to distribute governed data without exposing raw underlying storage.

How It Meets the Requirements

• Prevent Unauthorized Exfiltration: By publishing curated listings powered by authorized views or applying fine-grained row-level access control, subscribers only access computed or filtered data. Subscribers do not require direct read permissions on the source tables, ensuring raw transactional records remain isolated.
• Column-Level and Row-Level Security: Policy tags managed via Dataplex/Cloud Data Catalog enforce dynamic column masking on sensitive PII fields, while row-level filter expressions (row_access_policies) partition visibility by partner_tenant_id at query runtime.
• Subscriber Access & Analytics: Granting partner subscriber principals the BigQuery Data Viewer (roles/bigquery.dataViewer) role and subscription permissions on the Analytics Hub listing allows them to discover the listing and provision a linked dataset inside their own project without granting IAM access to the provider's upstream datasets.

Key Advantages and Capabilities

• Decoupled Compute Billing: Queries executed by partner subscribers against linked datasets consume compute resources and query slots within the subscriber's project.
• Centralized Policy Enforcement: Data masking and row filters apply dynamically regardless of whether access occurs internally or via Analytics Hub linked datasets.
• Least Privilege Access: The publisher retains complete control over dataset schemas, IAM permissions, and access grants without managing external IAM identities across underlying storage tables.
• No Data Duplication: Linked datasets provide real-time, zero-copy access to source updates without running custom scheduled replication pipelines.

Why This Is Optimal

This pattern adheres to Google Cloud security best practices by combining resource-level IAM boundaries with granular database engine security (column masking and row-level filtering). It prevents data replication drift while securing sensitive multi-tenant financial data under strict perimeter controls.