ThoughtSpot and Looker have detailed system activity logs; Power BI needs Azure Monitor for query-level detail; Basedash and Metabase (Pro) ship audit logs; database-layer designs also leave a trail in the database’s own query log. The strongest implementations read attributes from your identity provider (Okta, Entra ID, Google Workspace) via SAML or OIDC and support SCIM so group membership stays in sync. Does RLS cover dashboards, exports, scheduled deliveries, API calls, embedded sessions, and AI-generated queries? For a broader tool comparison on that warehouse, see best BI tools for Snowflake. Sigma, Omni, ThoughtSpot, Looker, Tableau, Power BI (DirectQuery), Lightdash, and Metabase all query Snowflake live and can layer their own attribute-based filters on top. The IS NULL branch keeps your application’s direct connections working unchanged; only connections that set the variable (Basedash) are filtered.
Power BI implements RLS through DAX role definitions in the semantic model. A Looker developer can add an access_filter in five minutes and it protects every Looker query path. Eight of the nine tools define RLS inside Unli Slots Casino the BI application. Prices are list prices from public pricing pages, verified September 2026; quote-based vendors are marked as such.
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.
Row-level security decides which records a user sees; column-level security decides which fields. Physical isolation gives you tenant separation without RLS, but it does not scale past a few hundred tenants, complicates cross-customer analytics, and multiplies migrations. The bypass risk is outside the tool, when the same user reaches the database through a SQL editor, a second BI tool, or a leaked credential. Always test by asking the AI for “all rows” as a restricted user; early natural-language features from several vendors did not enforce RLS consistently. Looker, ThoughtSpot Enterprise, Sigma, and Omni are quote-based and generally sized for larger teams. Basedash’s Startup plan ($1,000 per month for up to 25 users) includes RLS and flat pricing, so viewers are free to add.