Manpages for Snowflake Data Masking

Manpages for Snowflake Data Masking matter because clear documentation is the difference between safe and unsafe defaults. Snowflake provides dynamic data masking, policy creation, and policy assignment at the column, schema, or database level. Understanding the command syntax, parameters, and behavior saves time when you need precision.

A typical data masking workflow starts with creating a masking policy:

CREATE MASKING POLICY ssn_mask 
 AS (val STRING) 
 RETURNS STRING ->
 CASE 
 WHEN CURRENT_ROLE() IN ('FULL_ACCESS_ROLE') THEN val
 ELSE 'XXX-XX-XXXX'
 END;

This policy masks Social Security Numbers unless the role has full access. Policies can be stored and reused across tables, keeping rules consistent.

To attach the policy, run:

ALTER TABLE customers 
 MODIFY COLUMN ssn 
 SET MASKING POLICY ssn_mask;

Snowflake manpages explain each clause, including how masking interacts with views, clones, and role-based access. For heavy workloads, understanding the evaluation order is critical. Masking policies evaluate after row access checks, so data that passes the row filter is then masked as defined.

Manpages also cover advanced use: conditional masking, type-specific policies, and combining masking with secure views. Proper use reduces manual overhead and prevents accidental leaks during analytics or exports.

Snowflake’s data masking protects data without breaking queries. The tighter your policies, the stronger your control over compliance requirements like GDPR, HIPAA, or PCI DSS. Always test policies in staging to confirm exact behavior before production.

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