Part of the Analytify BI Glossary, clear definitions of the business intelligence, analytics, and modern data stack terms that matter.
Row-level security (RLS) is a database or BI-tool feature that filters which rows a user can see based on their identity, role, or session token, with the filter enforced at query time before the data leaves the database, rather than after the fact.
For multi-tenant SaaS analytics, row-level security is the foundational security primitive. When customer A logs in to your product, every query they run must be automatically scoped to WHERE tenant_id = "A" before the database executes it. The application layer cannot accidentally leak data because the filter is enforced at the database or BI tool layer.
Without RLS, multi-tenant analytics is one bug away from a data breach. Application-level filtering trusts the developer to remember the filter on every query; RLS enforces it automatically.
There are three common RLS implementation patterns in 2026:
The token-based pattern is what modern embedded analytics platforms like Analytify use. The BI tool never knows about your customers as users; it only knows about tenants as a JWT claim.
A SaaS analytics tool passes a signed JWT with tenant_id: "ACME" in the embed URL. The BI engine validates the JWT signature, extracts the claim, and appends WHERE tenant_id = "ACME" to every SQL query. If a developer mistakenly writes a query without the filter, the RLS policy adds it back. If the JWT is missing or invalid, the query returns nothing — fail-closed by default.
Common RLS implementations in modern BI:
Native row-level security policies attached to tables. Standard database feature since Postgres 9.5.
Snowflake-native row-level security with masking policies and dynamic data masking.
Per-row access controls applied via filtered tables and views.
Token-based RLS at the BI tool query layer, decoupled from your auth provider. Designed for SaaS multi-tenant embed.
LookML access_filter feature, tied to Looker user attributes.
Application-level filtering trusts the app code to set a tenant_id filter on every query; a single bug can leak data. RLS enforces the filter at the database or BI tool layer; bugs cannot bypass it.
Most do, but the implementation varies. Tableau and Power BI tie RLS to their user model. Looker uses LookML access filters. Modern embedded analytics platforms use JWT-based RLS that is decoupled from the BI tool user model.
Yes. Without RLS, multi-tenant SaaS analytics is a security vulnerability. Modern multi-tenant SaaS deployments enforce RLS at query time on every request.
RLS filters which rows a user can see (e.g. only their tenant’s rows). Column-level security masks specific columns (e.g. credit card numbers). Modern BI tools support both; they are complementary.
The application signs a JWT containing tenant_id and user role. The BI tool validates the signature, extracts the claims, and uses them to scope every SQL query at the warehouse. The BI tool never stores user data, only validates tokens.
Properly implemented, RLS adds negligible overhead because the filter is added at query parse time and benefits from indexed columns. Naive implementations (full-table scans before filtering) can be slow.