BigQuery Row-Level Security Implementation Guide

  • 11/2/2024
  • 4-minute read

TL;DR: Use the checklist below to configure Row Access Policies in under 10 minutes, then reuse the policy snippets to keep compliance officers and analysts aligned.

Quick-Start Checklist

  • Confirm your service or user accounts have bigquery.tables.setIamPolicy.
  • Identify the column you will filter on (department, region, tenant, etc.).
  • Decide which principals should see each slice of the data.
  • Create a Row Access Policy in the console or via SQL using the templates below.
  • Test queries as each role and document results for auditors.

Console Wizard Walkthrough

  1. Select dataset ➝ table ➝ Security ➝ Row-level security.
  2. Click Create policy and name it using filter_<dimension>_<region> or another predictable pattern.
  3. Add principals (users, groups, service accounts) that should access the rows.
  4. Paste the SQL predicate, for example department = SESSION_USER() or region IN UNNEST(@allowed_regions).
  5. Save, then use Preview as principal to confirm the filter behaves as expected.

SQL Quick Start

CREATE ROW ACCESS POLICY filter_by_department
ON dataset.employees
GRANT TO ('user:analyst@company.com', 'group:finance@company.com')
FILTER USING (department IN ('Finance', 'IT'));

Run the CREATE ROW ACCESS POLICY statement in the BigQuery editor or pipeline, then impersonate the target analyst or service account with bq query --impersonate-service-account (or Google Cloud CLI) to confirm only the intended rows return.

BigQuery Row-Level Security (RLS) is a critical security feature that enables granular access control at the row level. This guide will show you how to implement RLS effectively to protect sensitive data while maintaining operational efficiency.

Why Implement Row-Level Security in BigQuery?

  • Enhanced Data Protection: Control data access with precision
  • Compliance Ready: Meet GDPR, HIPAA, and other regulatory requirements
  • Simplified Administration: Manage security policies centrally
  • Cost-Effective: Implement security without additional infrastructure

Step-by-Step Implementation Guide

Basic Policy Creation

CREATE ROW ACCESS POLICY filter_by_department
ON dataset.employees
GRANT TO ('user:analyst@company.com', 'group:finance@company.com')
FILTER USING (department IN ('Finance', 'IT'));

Common Security Patterns

  1. Department-Based Access
department = SESSION_USER();
  1. Regional Access
region IN (SELECT allowed_regions FROM access_control.user_regions);
  1. Temporal Access
timestamp_field BETWEEN access_start AND access_end;

To enable BigQuery row-level security, you can follow these steps:

  1. Go to the BigQuery console and select the Datasets tab.
  2. Click the name of the dataset that you want to enable row-level security for.
  3. Click the Security tab.
  4. Click the Row-level security tab.
  5. Click the Create policy button.
  6. In the Create policy dialog, enter a name for your policy.
  7. In the Principals section, select the users or groups that you want to grant access to the data.
  8. In the Filter section, enter a SQL filter that defines the rows that the users or groups will be able to access.
  9. Click the Create button.

Once you have created a row-level security policy, users or groups that are not included in the policy will not be able to access the data in the table.

Here are some additional things to keep in mind when enabling row-level security:

  • Row-level security policies can coexist on a table with column-level security as well as dataset-level, table-level, and project-level access controls.
  • If you create a new row-level security policy to limit row access, users that previously had full access must be added to a TRUE filter to maintain their access.
  • Row-level security is enforced at query time, so users will only see the rows that they are authorized to access when they run queries against the table.

Best Practices

Security Checklist

  • Audit existing permissions
  • Document policy decisions
  • Test policies with sample queries
  • Monitor policy performance
  • Regular security reviews

Troubleshooting Common Issues

Policy Not Working?

  1. Verify policy syntax
  2. Check user permissions
  3. Review policy conditions
  4. Examine query patterns

Performance Optimization

  • Index relevant columns
  • Optimize filter conditions
  • Monitor query performance
  • Cache frequently accessed data

Row-Level Security FAQs

How are Row Access Policies different from Authorized Views?

Row Access Policies sit directly on the table and automatically scope every query. Authorized Views wrap the underlying table with a curated SELECT statement. Use policies when you want deterministic enforcement without maintaining separate datasets, and layer an Authorized View on top when analysts need a simplified schema.

What logging should I enable after deploying RLS?

Turn on Data Access logs in Cloud Logging and monitor cloudaudit.googleapis.com/data_access entries for your protected tables. Pair this with scheduled queries on INFORMATION_SCHEMA.ROW_ACCESS_POLICIES to track who updated policies and when.

How do I manage policies as code?

Export your CREATE ROW ACCESS POLICY statements into Terraform or a deployment script and keep them in source control. Teams often parameterize the policy filter and principal list so staging and production stay aligned.

Additional Resources

Note: Examples and features are current as of February 2024. Check documentation for updates.

cloudsecuritydatabigqueryaccess-control