Sharing and collaborating securely with Row-Level Security (RLS) in Power BI is a vital practice for protecting sensitive data while enabling personalized data access for different users or groups.
RLS restricts data visibility within reports and dashboards by filtering data dynamically based on the user’s identity, ensuring individuals only see data they are authorized to access.
Implementing RLS allows organizations to enforce data governance, comply with privacy regulations, and foster trust while maintaining the collaborative and interactive nature of Power BI reporting.
RLS is configured by defining security roles in Power BI Desktop using DAX filter expressions on tables, and then assigning users or Azure Active Directory (AAD) groups to these roles in the Power BI Service.
This approach enables data differentiation without multiplying report versions or datasets.
Combined with workspaces, apps, and sharing capabilities, RLS offers a robust framework for scalable and secure BI deployments supporting diverse teams and stakeholders with tailored data views.
Key Concepts of Row-Level Security (RLS)
Row-Level Security enables fine-grained data access within shared reports and dashboards. Listed below are the key concepts that define how RLS works in Power BI.
1. Role Definition
Create roles in Power BI Desktop with DAX filters restricting rows based on user or attribute values (e.g., region, department).
Example: Sales[Region] = "West" limits data to Western region sales only.
2. Role Assignment
After publishing, assign users or groups to roles using Power BI Service’s security settings.
Supports Azure AD groups for scalable management.
3. Dynamic Row Filtering
Utilize UserPrincipalName() function in DAX to dynamically filter data based on logged-in user email or username.
Example:
Sales[SalesPersonEmail] = USERPRINCIPALNAME() 4. Testing and Validation:
Test roles within Power BI Desktop using the “View as Role” feature before publishing.
Validate user access in Power BI Service by simulating role membership.
Best Practices for Secure RLS Implementation
Strong RLS practices help protect sensitive data while enabling controlled data access. The list below highlights key techniques for implementing secure and scalable RLS.
1. Design Roles Based on Business Logic: Align security filters with organizational hierarchy or data governance policies.
2. Use Dynamic RLS for Scalability: Prefer user-based filtering rather than hardcoding usernames, simplifying maintenance.
3. Minimize Role Complexity: Avoid overly complex DAX filters that impact performance.
4. Combine RLS with Object-level Security: Hide sensitive columns along with row filtering for enhanced protection.
5. Document Security Rules: Maintain clear documentation for compliance and audit purposes.
Collaboration With RLS
1. Users with assigned roles can safely share reports and dashboards, confident that underlying data remains within authorization boundaries.
2. RLS supports multiple users accessing the same report with different data views, enhancing collaboration without data duplication.
3. Integration with Power BI Apps and Workspaces allows controlled distribution and management within teams.