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.
Why Row-Level Security (RLS) Matters
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.
How Row-Level Security (RLS) Works
There are three common RLS implementation patterns in 2026:
- Database-native RLS: Postgres, BigQuery, Snowflake, and other modern warehouses support row-level security policies as a database feature. The policies attach to tables and apply automatically based on the current session’s user or role.
- BI-tool RLS: Most BI tools (Tableau, Looker, Metabase Pro, Power BI) implement their own RLS. The filter is appended to queries before they reach the warehouse.
- Token-based RLS (recommended for SaaS embed): Your application signs a JWT containing tenant_id and user role. The BI tool validates the JWT and uses its claims to scope every query. This decouples the BI tool’s identity layer from your SaaS identity layer, which is critical at scale.
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.
Real-World Example
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 Row-Level Security (RLS) Tools and Platforms in 2026
Common RLS implementations in modern BI:
Postgres RLS
Native row-level security policies attached to tables. Standard database feature since Postgres 9.5.
Snowflake Row Access Policies
Snowflake-native row-level security with masking policies and dynamic data masking.
BigQuery Row-Level Security
Per-row access controls applied via filtered tables and views.
Analytify (JWT-based)
Token-based RLS at the BI tool query layer, decoupled from your auth provider. Designed for SaaS multi-tenant embed.
Looker access filters
LookML access_filter feature, tied to Looker user attributes.
Frequently Asked Questions About Row-Level Security (RLS)
How is RLS different from application-level filtering?
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.
Do all BI tools support RLS?
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.
Is RLS required for SaaS embedded analytics?
Yes. Without RLS, multi-tenant SaaS analytics is a security vulnerability. Modern multi-tenant SaaS deployments enforce RLS at query time on every request.
What is the difference between RLS and column-level security?
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.
How does JWT-based RLS work?
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.
Does RLS impact query performance?
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.