Scalable row-level security (RLS) implementation in Power BI

Row-level security (RLS) enables scalable, secure analytics when integrated with governance frameworks across enterprise environments.

As energy enterprises modernize their reporting infrastructure, one persistent challenge remains—ensuring that sensitive data is accessible only to those who need it. With Power BI widely adopted for enterprise reporting, managing secure and role-specific access becomes critical. Row-Level Security (RLS) addresses this by restricting visibility to specific data rows based on user attributes such as role, geography, or business unit—reducing risk and increasing trust in data-driven decisions.  

Static vs. Dynamic RLS 

Power BI offers two models for RLS: static and dynamic. Static RLS involves manually assigning users to roles, which may work for smaller, stable teams but quickly becomes inefficient at scale. Dynamic RLS uses built-in functions like USERPRINCIPALNAME() to detect the current user and apply access filters through a centralized security mapping table. This approach is more scalable and adaptable to the evolving access requirements of large enterprises.  

Implementing Dynamic RLS 

Dynamic RLS is built on three core components: 

• Identifying the user with Power BI functions. 

• Mapping users to authorized data segments via a security table. 

• Establishing data model relationships to ensure filters apply correctly across all tables. 

Precision is critical—any misalignment can result in either overly restrictive access or unintended data exposure. 

Scaling for performance 

At enterprise scale, RLS implementations often involve many-to-many user relationships. To handle this, teams use bridge tables and flattened mappings to streamline access logic. Performance tuning is also key: leveraging star schema design, optimized DAX measures, query folding, and incremental refresh ensures responsive reporting experiences.  

Governance and compliance 

RLS should be integrated into the broader data governance framework. Documented access policies, regular reviews, and integration with Azure Active Directory help automate and secure access control. RLS also supports compliance with regulations such as NERC CIP, ISO 27001, and GDPR.  

Real-world use case: Managing granularity challenges 

In enterprise-level reporting within industries like energy and manufacturing, varying data granularity can complicate Row-Level Security (RLS) implementation. By restructuring the user access model and using bridge tables to unify data, security filters were applied consistently.  

When implemented with precision and embedded in governance, dynamic RLS becomes a strategic enabler—supporting secure, scalable analytics across complex energy enterprises.