Power BI App Security: Access & Models

Key insights

• App Workspace vs App: Explain the difference between the workspace where reports and datasets are developed and the published App that end users consume.
  Assign people to workspace roles (Admin, Member, Contributor, Viewer) and use Azure AD groups to simplify access control.
• Dataset & Semantic Model: Control dataset permissions because the Semantic Model defines what data and measures users can reuse in new content.
  Grant Build permission when you want others to create reports from a dataset, and mark trusted datasets as Certified or Promoted for reuse.
• Row-Level Security (RLS) & Object-Level Security (OLS): Use RLS to filter rows per user role and OLS to hide tables or columns from specific users.
  Always test roles with “Test as role” and avoid giving broad admin rights that bypass RLS rules.
• Licensing and Capacity: Know the difference between Power BI Pro and Premium—Pro is required for most sharing between creators, while Premium enables broad distribution without per-user Pro licenses and unlocks large-model features.
• Sensitivity Labels and Governance: Apply Sensitivity Labels to datasets and reports to protect sensitive data and enforce policies.
  Use separate dev and prod workspaces, limit export permissions, and track activity with audit logs to maintain governance.
• Automation and Admin Tools: Use Service Principals and Azure AD Groups to automate deployments and manage access at scale.
  Implement deployment pipelines and CI/CD practices to test changes before publishing apps to users.

Keywords

Power BI app security, Power BI access control, Power BI semantic model, Power BI row-level security, Power BI app permissions, Power BI dataset security, Power BI workspace security, Power BI security best practices