Snowflake Employee Data Protection

Learn how to prevent exposure of employee data in Snowflake environments. Follow step-by-step guidance for GDPR compliance.

data exposure

GDPR

snowflake

Why It Matters

The core goal is to proactively secure employee information stored within your Snowflake environment before unauthorized access occurs. Preventing employee data exposure in Snowflake is critical for organizations subject to GDPR, as it helps you demonstrate compliance with data protection principles—mitigating the risk of data breaches and regulatory penalties.

Primary Risk: Data exposure of employee information

Relevant Regulation: GDPR General Data Protection Regulation

A comprehensive prevention strategy establishes robust access controls, implements data masking, and enables continuous monitoring to maintain security posture.

Prerequisites

Permissions & Roles

  • Snowflake ACCOUNTADMIN or SECURITYADMIN role
  • USAGE privileges on databases and schemas
  • Ability to create masking policies and row access policies

External Tools

  • Snowflake CLI or SnowSQL
  • Cyera DSPM account
  • API credentials

Prior Setup

  • Snowflake account provisioned
  • RBAC hierarchy established
  • Network policies configured
  • Data classification enabled

Introducing Cyera

Cyera is a modern Data Security Posture Management (DSPM) platform that discovers, classifies, and continuously monitors your sensitive data across cloud services. By leveraging advanced AI and Named Entity Recognition (NER) models, Cyera automatically identifies employee data patterns in Snowflake tables and columns, enabling you to implement targeted prevention controls and maintain GDPR compliance through automated policy enforcement.

Step-by-Step Guide

1
Implement Role-Based Access Control (RBAC)

Create granular roles for HR data access, assign minimum necessary privileges, and establish a clear role hierarchy that limits employee data access to authorized personnel only.

CREATE ROLE hr_data_reader;
GRANT USAGE ON DATABASE hr_db TO ROLE hr_data_reader;

2
Configure Dynamic Data Masking

Create masking policies for employee PII fields like SSNs, addresses, and phone numbers. Apply these policies to sensitive columns to automatically obfuscate data for unauthorized users.

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

3
Enable Row-Level Security

Implement row access policies to ensure users can only see employee records they're authorized to access, such as their own records or those within their department.

CREATE ROW ACCESS POLICY employee_policy AS (department string) RETURNS boolean ->
CURRENT_ROLE() = 'HR_ADMIN' OR department = CURRENT_USER();

4
Integrate Cyera for continuous monitoring

Connect Cyera to your Snowflake environment to automatically classify employee data, monitor access patterns, and alert on potential exposure risks. Configure automated policy suggestions based on data discovery.

Architecture & Workflow

Snowflake RBAC

Controls who can access employee data

Dynamic Data Masking

Obfuscates sensitive fields in real-time

Row-Level Security

Filters records based on user context

Cyera Monitoring

Continuous classification and alerting

Security Flow Summary

User Query → RBAC Check → Row Filter → Data Masking

Best Practices & Tips

Access Control Design

  • Follow principle of least privilege
  • Use functional roles over individual grants
  • Regularly audit role assignments

Masking Strategy

  • Test masking policies in development first
  • Consider partial masking for operational needs
  • Document masking rules for compliance

Common Pitfalls

  • Forgetting to apply policies to views
  • Over-privileged service accounts
  • Missing row-level security on shared tables

References & Further Reading

Next Steps

🔍 Detect: Scan for employee data in Snowflake 🔧 Fix: Remediate exposed employee data