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A data engineer is designing security controls for an enterprise Looker deployment that queries a central data warehouse. The implementation must satisfy the following governance requirements:
Which combination of Looker access controls and LookML configurations should the data engineer implement?
This approach leverages Looker's native governance framework by combining role-based access control (RBAC), user attributes, and declarative LookML security parameters (access_filter and access_grant) to enforce granular data access.
access_filter parameter binds an Explore dimension directly to a Looker user attribute (e.g., sales_region). When a user runs a query, Looker automatically injects a parameterized WHERE clause into the generated SQL, restricting row-level data access seamlessly.access_grant defined at the model level checks a user attribute (e.g., department: finance). By applying required_access_grants on specific dimensions or measures, Looker hides those fields completely from unauthorized users in the field picker and prevents them from querying that data.access_filter) ensure that filtering cannot be bypassed or removed by report viewers or dashboard creators.This is the native, recommended architectural pattern in Looker for multi-tier access control. It fully satisfies model restriction, row restriction, and field restriction within a unified semantic layer without code duplication or database-side schema sprawl.
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