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Your enterprise stores sensitive sales and customer data in BigQuery. The dataset implements row-level security (RLS) to restrict sales data access based on each manager's region (SESSION_USER()), along with column-level security policy tags to mask personally identifiable information (PII).
You need to configure access for business intelligence (BI) visualization dashboards in Looker Studio so that multiple regional sales managers can view their respective metrics securely while strictly adhering to the principle of least privilege.
Which configuration should you implement?
Viewer's Credentials (OAuth) require individual dashboard viewers to authenticate against Google Cloud BigQuery using their own identity rather than a shared service account. Combining this credential model with predefined IAM roles at specific resource hierarchy levels enforces least-privilege access and activates fine-grained security policies.
SESSION_USER(). This ensures that regional sales managers see only the rows authorized by the row-level security (RLS) policy.roles/bigquery.jobUser on the project provides only the ability to run query jobs without exposing data, while roles/bigquery.dataViewer is restricted specifically to the target dataset rather than the entire project or organization.bigquery.jobs.create) from dataset-level storage read permissions (bigquery.tables.getData).Using Owner's Credentials or a single service account bypasses user-specific row-level security and column-level policy tags because BigQuery sees only the service identity. Delegating the query context via Viewer's (OAuth) credentials with narrow dataset-scoped roles guarantees that security controls and least privilege are enforced natively at query execution time.
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