Implementing Row-Level Security in Power BI
Secure your Power BI reports with proper row-level security implementation for enterprise deployments.
Row-Level Security (RLS) ensures users only see data they are authorized to access. For enterprises handling sensitive data, RLS is not optional - it's essential. Our Power BI consulting team can help you implement RLS correctly from day one.
What is Row-Level Security?
RLS restricts data access at the row level based on user identity. Unlike report-level permissions, RLS ensures users only see their authorized data even within the same report.
Key Benefits: - Data Protection - Users cannot see unauthorized rows - Single Report Distribution - One report serves all users - Audit Compliance - Meet regulatory requirements (HIPAA, GDPR, SOC 2) - Reduced Report Sprawl - No need for department-specific report copies
Static vs Dynamic RLS
Static RLS Hardcoded filter values for specific roles: - Sales East Role: [Region] = "East" - Sales West Role: [Region] = "West" - Best for simple scenarios with few roles
Dynamic RLS Filter based on user identity using DAX: - [SalesRepEmail] = USERPRINCIPALNAME() - [ManagerID] IN PATHCONTAINS(UserHierarchy, CurrentUserID) - Scales to thousands of users automatically
Implementation Steps
Step 1: Design Your Security Model Create a security table mapping users to their data access: - User email column (matches Azure AD) - Data filter columns (Region, Department, etc.) - Consider hierarchical access (managers see team data)
Step 2: Build Relationships Connect your security table to fact tables through dimension tables. Follow star schema design principles for optimal performance.
Step 3: Create Roles in Power BI Desktop 1. Go to Modeling > Manage Roles 2. Create a new role 3. Add DAX filter expressions 4. Reference your security table
Step 4: Test with "View as Role" Before publishing, test thoroughly: - Use View as Role to simulate users - Test edge cases (new users, multiple roles) - Verify data boundaries are correct
Step 5: Assign Users in Power BI Service After publishing, assign Azure AD users or groups to roles. Groups are recommended for easier management.
Step 6: Monitor and Audit Use Power BI audit logs to monitor: - Who accessed which reports - RLS filter execution - Failed access attempts
Best Practices
- Use dynamic RLS for scalability
- Test extensively before production
- Document role logic for compliance audits
- Combine with workspace security for defense in depth
- Monitor performance - complex RLS can slow reports
Common Pitfalls to Avoid
- Not testing all user scenarios
- Forgetting bi-directional filters on relationships
- Complex DAX that hurts performance
- Missing edge cases like new employees
RLS in Microsoft Fabric
Microsoft Fabric extends RLS capabilities with: - Object-Level Security (OLS) for columns - Direct Lake RLS support - Unified governance across all workloads
Need help implementing RLS? Contact us for a security assessment.
Frequently Asked Questions
Can RLS be bypassed by users?
No, RLS is enforced at the engine level. Users cannot bypass it through DAX queries, exports, or API access. Even Analyze in Excel respects RLS filters.
Does RLS work with DirectQuery?
Yes, RLS works with both Import and DirectQuery modes. For DirectQuery, the RLS filter is pushed to the source database for processing.
How many roles can I create?
There is no hard limit on roles, but for scalability, use dynamic RLS with a security table instead of creating many static roles.