Best Bi Tools For Row-level Security 2026

In-memory engines (Power BI import mode) evaluate the filter quickly; DirectQuery and live-query tools push it to the database, where an indexed equality filter is cheap and a per-row function call is not. For enforcement that does not depend on the BI tool, database-layer RLS in PostgreSQL (used by Basedash through the basedash.groups session variable) or Snowflake row access policies are stronger because every client is filtered. If you embed dashboards in your product, the tool must accept identity from your application (JWT, signed embed URL, trusted authentication) and set attributes per session. Snowflake has its own row access policies, and the simplest secure design is to enforce them in Snowflake and have the BI tool connect with a role or session context Snowflake can evaluate. Avoid Looker, ThoughtSpot Enterprise, and Tableau Enterprise at this stage unless a specific integration requires them; their RLS is excellent but the pricing and modeling overhead are sized for larger teams. Sandboxes restrict rows and can hide columns, and they apply to embedded dashboards and to Metabot AI questions.

When any user runs a query, Basedash Unli Slots Casino sets a PostgreSQL session variable, basedash.groups, to the comma-separated list of groups that user belongs to in the workspace. The weakness is maintenance, since sandboxes are saved queries rather than declarative filters. The filter is part of the same version-controlled semantic layer that Lightdash’s AI agents query. Because workbooks and AI queries resolve through that model, the filter applies to ad hoc exploration and to embedded dashboards where the embed session sets the attributes. Omni defines access filters on user attributes in its shared model, the governed layer that sits between the raw schema and workbooks. Spotter is not included on the entry $25 per user per month tier; the $50 tier includes 25 Spotter queries per user per month.

RLS for other databases is not available yet; those sources use the database’s own access controls plus Basedash’s data source and table permissions. The managed Basedash Warehouse runs on DuckDB, so it is not covered by this PostgreSQL policy model and uses Basedash’s data source and table permissions instead. The policies filter AI chat answers, dashboards, scheduled automations, and Slack queries because PostgreSQL applies them, not the app. Basedash sets a basedash.groups session variable on every query with the user’s group memberships, and you write PostgreSQL policies that reference it.