Row-Level Security (RLS) vs Object-Level Security (OLS) in Power BI

Power BI provides robust security features that allow organizations to control data access at various levels. Two key mechanisms for securing data in Power BI are Row-Level Security (RLS) and Object-Level Security (OLS).

1. Row-Level Security (RLS)

What is RLS? RLS restricts access to data at the row level based on user roles. This ensures that users see only the data they are authorized to view without creating multiple reports for different audiences.

Implementing RLS in Power BI

🔹 Static RLS – Manually assign user roles and filter specific data.

🔹 Dynamic RLS – Uses a security table with usernames mapped to their respective access levels.

Steps to Set Up RLS in Power BI Desktop

1. Create Roles: In Modeling → Manage Roles, define filtering conditions using DAX expressions. Example:
2. Assign Users: In the Power BI Service, assign users to the created roles.
3. Test Security: Use View as Role to verify if access is properly restricted.

Use Cases of RLS

✅ Restrict sales data by region (e.g., Sales Managers see only their regions).

✅ Show employee records only to relevant department heads.

✅ Limit financial data access based on user roles (e.g., Executives vs. Staff).

2. Object-Level Security (OLS)

What is OLS? OLS restricts access to specific tables and columns in Power BI datasets. Unlike RLS, which filters data within tables, OLS completely hides tables/columns from unauthorized users.

Implementing OLS in Power BI

1. Use Tabular Editor: OLS settings are configured using Tabular Editor (not directly in Power BI Desktop).
2. Set Permissions: Define Read or None access on specific tables/columns.
3. Deploy & Assign Roles: Apply the OLS configuration to your dataset in Power BI Service.

Use Cases of OLS

✅ Hide sensitive salary columns from general employees.

✅ Restrict financial data tables for non-finance personnel.

✅ Prevent access to confidential business KPIs while allowing general reporting.