Implementing Row-Level Security in Snowflake: A Complete Guide

What Is Row-Level Security in Snowflake?

Row-level security (RLS) in Snowflake allows administrators to enforce fine-grained data access policies so that different users querying the same table automatically see only the rows they are authorized to view. Snowflake implements RLS through row access policies, which are reusable expressions attached to tables or views that evaluate at query time based on the current CURRENT_ROLE() or other session parameters.

Why Row-Level Security Matters

Modern analytics platforms ingest multi-tenant or highly sensitive datasets—think customer records, payroll, healthcare, or financial transactions. Duplicating tables for every tenant or permission group leads to data sprawl, complex ETL, and nightmare maintenance. Row-level security solves this by:

• Reducing Data Silos – One canonical table serves all users.
• Simplifying Governance – Consistent policies live next to the data definition.
• Limiting Blast Radius – Least-privilege access is enforced automatically.
• Lowering Cost – Fewer copies means lower storage and compute.

How Snowflake Implements Row Access Policies

Policy Object

A ROW ACCESS POLICY is a named object containing a Boolean SQL expression that returns TRUE when a row is visible to the caller. Policies are created once and can be attached to multiple tables or views.

CREATE OR REPLACE ROW ACCESS POLICY rlp_customer_tenant
AS (tenant_id STRING) RETURNS BOOLEAN ->
tenant_id = CURRENT_ROLE();

Evaluation Flow

1. User submits a query via any client (e.g., Galaxy SQL Editor).
2. Snowflake inspects the metadata to see if a row access policy is attached to queried objects.
3. The policy expression is appended as an implicit WHERE clause.
4. Policy executes inside Snowflake’s virtual warehouse; results are filtered before they leave the cluster.

Step-by-Step Implementation Guide

1. Define Roles and Grants

CREATE ROLE tenant_a;
CREATE ROLE tenant_b;
-- Grant minimal privileges
GRANT USAGE ON DATABASE analytics TO ROLE tenant_a;
GRANT SELECT ON TABLE analytics.public.customers TO ROLE tenant_a;

2. Create the Row Access Policy

CREATE OR REPLACE ROW ACCESS POLICY rlp_customers_tenant
AS (tenant_id STRING) RETURNS BOOLEAN ->
tenant_id = CURRENT_ROLE();

3. Attach Policy to the Table

ALTER TABLE analytics.public.customers
ADD ROW ACCESS POLICY rlp_customers_tenant
ON (tenant_id);

4. Test the Policy

-- Assume role Tenant A
USE ROLE tenant_a;
SELECT tenant_id, customer_name FROM analytics.public.customers;
-- Only rows where tenant_id = 'TENANT_A' are returned

Detailed Example: Multi-Region Sales Data

Sample Dataset

Assume a sales table with columns order_id, region, sales_amount, and sales_rep_id. Regional managers should see only their region.

Policy Definition

CREATE OR REPLACE ROW ACCESS POLICY rlp_sales_region
AS (region STRING) RETURNS BOOLEAN ->
region = CURRENT_REGION();

Attach & Verify

ALTER TABLE sales ADD ROW ACCESS POLICY rlp_sales_region ON (region);

-- Northeast manager
ALTER ROLE northeast_mgr SET TAG region = 'NE';
USE ROLE northeast_mgr;
SELECT SUM(sales_amount) FROM sales; -- returns NE data only

Best Practices

Use Deterministic Functions

Snowflake caches query results aggressively. Ensure policy predicates rely on deterministic functions like CURRENT_ROLE() or session variables to avoid cache leakage.

Centralize Policy Objects

Create policies in a dedicated SECURITY schema so they’re easy to audit and version-control.

Combine with Column-Level Security

Pair RLS with MASKING POLICIES for a defense-in-depth approach.

Common Mistakes and How to Avoid Them

Mis-ordering Arguments

Mistake: Policy signature does not match columns referenced in ON() clause.
Fix: Make sure argument order and data types align exactly.

Forgot to Grant Policy Privileges

Mistake: Users can’t query table after policy applied because missing APPLY privilege.
Fix: Grant APPLY ROW ACCESS POLICY to necessary roles.

Using Non-Deterministic Functions

Mistake: Policy references CURRENT_TIMESTAMP() causing inconsistent caching.
Fix: Stick to deterministic or session-stable functions.

Galaxy Workflow for RLS

Because Galaxy is a modern SQL IDE, defining and testing row access policies is straightforward:

• Write the CREATE ROW ACCESS POLICY statement in a Galaxy tab with AI Copilot autocompleting role names and column lists.
• Save the script to a Collection called “Security Policies” so teammates can review and endorse.
• Run the script in different Connection Contexts—Galaxy lets you switch roles quickly to validate policy behavior without leaving the editor.

Monitoring & Auditing

Snowflake’s QUERY_HISTORY and ACCESS_HISTORY views record which policies filtered what data. Periodically review these logs or set up alerts when policy changes occur.

Conclusion

Row-level security in Snowflake delivers powerful, maintainable, and performant fine-grained access control. By encapsulating business rules in reusable policies and leveraging role-based evaluations, teams can keep a single source of truth while meeting stringent compliance requirements. Tools like Galaxy further streamline development, testing, and collaboration around these policies, helping data engineers ship secure data products faster.

Additional Resources

• Snowflake Docs – Row Access Policies
• Galaxy Blog – “Testing Data Security with AI Copilot”