Snowflake PII Exposure Remediation

Why It Matters

The core goal is to immediately remediate exposed PII within your Snowflake environment, implementing proper access controls and data masking to prevent unauthorized access. Fixing PII exposures in Snowflake is critical for organizations subject to GDPR, as it helps you demonstrate compliance with data protection requirements and prevent costly regulatory penalties.

Primary Risk: Unrestricted public access to PII data

Relevant Regulation: GDPR General Data Protection Regulation

Swift remediation through dynamic data masking, row-level security, and proper access controls ensures your PII remains protected while maintaining operational efficiency.

Prerequisites

Permissions & Roles

ACCOUNTADMIN or SECURITYADMIN role
CREATE MASKING POLICY privileges
CREATE ROW ACCESS POLICY privileges

External Tools

Snowflake SQL CLI or web interface
Cyera DSPM account
Data governance framework

Prior Setup

Snowflake Enterprise Edition or higher
PII exposure assessment completed
Database and schema structure documented
Business requirements for data access defined

Introducing Cyera

Cyera is a modern Data Security Posture Management (DSPM) platform that uses advanced AI and Natural Language Processing (NER) to automatically identify PII exposures in your Snowflake environment. Cyera's intelligent remediation engine not only detects exposed PII but also provides automated policy recommendations and helps implement dynamic data masking rules tailored to your specific data patterns and compliance requirements.

Step-by-Step Guide

Create dynamic data masking policies

Implement masking policies for different PII types. Start with common patterns like email addresses, phone numbers, and SSNs.

CREATE MASKING POLICY email_mask AS (val string) RETURNS string ->
CASE
WHEN CURRENT_ROLE() IN ('ANALYST_ROLE') THEN val
ELSE REGEXP_REPLACE(val,'(.)@(.)','@')
END;

Apply masking policies to affected columns

Use Cyera's findings to systematically apply masking policies to all identified PII columns across your databases and schemas.

ALTER TABLE customer_data MODIFY COLUMN email
SET MASKING POLICY email_mask;

Implement row-level security policies

Create row access policies to restrict data access based on user roles and context, ensuring users only see data they're authorized to access.

CREATE ROW ACCESS POLICY customer_policy AS (customer_region string) RETURNS boolean ->
CASE
WHEN CURRENT_ROLE() = 'GLOBAL_ADMIN' THEN TRUE
WHEN CURRENT_ROLE() = 'EU_ANALYST' AND customer_region = 'EU' THEN TRUE
ELSE FALSE
END;

Validate and monitor access patterns

Review query history and access patterns to ensure masking policies are working correctly. Set up alerts for policy violations and adjust permissions as needed.

Architecture & Workflow

Snowflake Data Platform

Source databases containing exposed PII

Cyera AI Engine

Identifies PII patterns and suggests remediation

Dynamic Data Masking

Real-time masking based on user context

Row Access Policies

Granular access control at row level

Remediation Flow Summary

Identify Exposures → Create Policies → Apply Controls → Monitor Access

Best Practices & Tips

Policy Management

Start with restrictive policies and gradually relax
Use conditional masking for different user types
Document all policy decisions and exceptions

Performance Optimization

Test masking policies on small datasets first
Monitor query performance after policy application
Use efficient masking functions to minimize overhead

Common Pitfalls

Forgetting to apply policies to new tables/columns
Over-masking data needed for legitimate business use
Not testing policy changes in non-production first

References & Further Reading

Next Steps

🛡️ Prevent: Set up proactive PII protection controls 🔍 Detect: Implement ongoing PII discovery scanning