Implementing Row-Level Security in Snowflake

Row-level security (RLS) in Snowflake lets you present different slices of the same table to different users without duplicating data, dramatically simplifying multi-tenant and compliance-driven architectures.

What Is Row-Level Security in Snowflake?

Row-level security is a fine-grained access-control mechanism that filters rows returned by a query according to policies you create. Unlike table- or column-level permissions, RLS ensures that every SELECT, UPDATE, or MERGE executed on the protected object automatically enforces the policy—no matter which SQL client or BI tool is used.

Why You Should Care

Modern data platforms often serve multiple customers, departments, or regulatory regions from a single Snowflake account. Without RLS you would need to manage dozens of derived tables or specialized views, leading to:

• Data duplication and increased costs
• Complex ETL pipelines
• Higher risk of exposing sensitive data through ad-hoc queries

RLS solves these issues with centrally managed policies that scale across thousands of tables and virtual warehouses.

How Row-Level Security Works Internally

Behind the scenes, Snowflake turns each policy into a Boolean expression that is injected into every query plan referencing the protected object. The expression can reference:

• Session context (e.g., CURRENT_ROLE(), CURRENT_USER())
• Custom session variables (SESSION:<var_name>)
• Metadata columns and deterministic functions

If the predicate evaluates to TRUE, the row is returned; otherwise, the row is silently filtered out.

Step-by-Step Implementation Guide

1. Design a Central Mapping Table

Start by defining who can see what. A simple pattern is a mapping table with user_name, tenant_id, and optional role columns.

CREATE OR REPLACE TABLE access_map (
user_name STRING,
tenant_id STRING,
allowed BOOLEAN DEFAULT TRUE
);

2. Create the Row Access Policy

CREATE OR REPLACE ROW ACCESS POLICY tenant_rls
AS (tenant_id STRING)
RETURN CASE
WHEN EXISTS (
SELECT 1
FROM access_map m
WHERE m.user_name = CURRENT_USER()
AND m.tenant_id = tenant_id
AND m.allowed
) THEN TRUE
ELSE FALSE
END;

This policy checks the mapping table for a match between the current user and the row’s tenant_id.

3. Attach the Policy to a Table or View

ALTER TABLE raw_events
ADD ROW ACCESS POLICY tenant_rls ON (tenant_id);

Snowflake immediately begins enforcing the policy for every query on raw_events.

4. Test the Policy

-- As user alice@acme.com
SELECT COUNT(*) FROM raw_events; -- Returns only ACME rows

-- As user bob@globex.com
SELECT COUNT(*) FROM raw_events; -- Returns only GLOBEX rows

5. Manage at Scale with Policy Tagging

Attach the same tenant_rls policy to hundreds of tables using scripting or Terraform. Because the predicate contains no table-specific references, it is reusable across your schema.

Advanced Techniques

Dynamic Data Masking + RLS

Combine RLS with masking policies to hide sensitive columns (e.g., PII) inside the permitted rows. This dual layer covers both horizontal and vertical security.

Session Variables for Temporality

Set SESSION:as_of_date to implement time-travel-aware RLS, returning only rows valid for a given business date.

Policy Inference on Views

When you secure a base table, Snowflake automatically enforces the same policy on any view that selects from that table—no extra work required.

Best Practices

• Centralize mapping tables in a dedicated SECURITY schema and grant SELECT only to administrators.
• Reference CURRENT_ROLE() rather than CURRENT_USER() when your organization relies on role-based access control.
• Document policies with COMMENT ON POLICY so auditors can trace intent.
• Write unit tests in SQL; use SNOWFLAKE.ACCOUNT_USAGE.POLICY_REFERENCES to inventory coverage.

Common Pitfalls and How to Avoid Them

• Using non-deterministic functions — Expressions like RANDOM() break policy caching. Stick to deterministic logic.
• Joining to large mapping tables in the predicate — Simplify or pre-aggregate mapping data to minimise query rewrite overhead.
• Forgetting to protect staging tables — Secure raw, staging, and analytics layers alike; otherwise savvy users may bypass RLS via CTAS.

Real-World Use Case: Multi-Tenant SaaS

A Series-B SaaS company stores all customer events in raw_events with a tenant_id column. By applying the tenant_rls policy, customer success managers querying through Galaxy see only their own customer’s data, while internal analysts with elevated roles can see cross-tenant metrics. No code changes in the application layer were required.

Using Galaxy With Snowflake Row-Level Security

Galaxy is a modern SQL editor that respects Snowflake’s permission model. Because the RLS predicate is enforced directly in Snowflake, every query executed from Galaxy automatically inherits the same protections. Power users can:

• Run ad-hoc queries knowing they can’t leak data across tenants
• Share endorsed queries in Galaxy Collections without exposing private rows
• Rely on the AI copilot to optimise queries without compromising security

No additional configuration is needed inside Galaxy—simply log in with a role that has the right Snowflake grants.

Conclusion

Row-level security in Snowflake is a powerful, declarative way to safeguard sensitive data while maximising analyst agility. By following the design patterns and best practices above, you can implement scalable, auditable policies that work seamlessly with tools like Galaxy.