Enforcing Security in Power BI Semantic Models

Data Ingestion and Semantic Models with Microsoft Fabric

Alex Kuntz

Head of Cloud Curriculum, DataCamp

Overview of Security in Semantic Models

Overview

  • Controls data access at different levels
  • Prevents unauthorized access to sensitive data

Types of Security in Semantic Models:

  • RLS: Restricts data at row level
  • OLS: Restricts access to entire objects (tables/columns)

Semantic model Security

Data Ingestion and Semantic Models with Microsoft Fabric

Row Level Security

Overview

  • Controls data visibility based on roles

Features

  • Restricts data at the row level
  • Configured using DAX filters
  • Ideal for user-specific data access
  • (e.g., region-based data access)

Row Level Security

Data Ingestion and Semantic Models with Microsoft Fabric

Object Level Security

Restricts access to entire objects (tables/columns)

Features

  • Allows securing specific data objects.
  • Useful for hiding sensitive information like salaries or personal details.
  • Requires configuration via Tabular Editor, not directly in Power BI.

Object Level Security

Data Ingestion and Semantic Models with Microsoft Fabric

Validating Security Roles (RLS and OLS)

  • Validation allows you to simulate data access as a specific role or user.

  • Add members to roles for precise access control.

  • Use Test as Role to test visibility for different roles in your model.

Adding members and Validating Reports

  • Ensure that RLS filters rows and OLS hides columns or tables.
Data Ingestion and Semantic Models with Microsoft Fabric

Comparing RLS and OLS

  • Filters specific rows in the data
  • Configuration: Managed within Power BI, no external tool needed.

Power BI

  • Effect: Filters data while keeping structure intact

RLS Semantic Model

  • Hides or restricts entire tables or columns
  • Configuration: Requires Tabular Editor or other external tools

Tabular Editor 2

  • Effect: Completely removes objects from view, altering the model structure

OLS Semantic Model

Data Ingestion and Semantic Models with Microsoft Fabric

Let's practice!

Data Ingestion and Semantic Models with Microsoft Fabric

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