Snowflake Customer Data Detection

Learn how to detect customer data in Snowflake environments. Follow step-by-step guidance for GDPR compliance.

data exposure

GDPR

snowflake

Why It Matters

The core goal is to identify every location where customer information is stored within your Snowflake environment, so you can remediate unintended exposures before they become breaches. Scanning for customer data in Snowflake is a priority for organizations subject to GDPR, as it helps you prove you've discovered and accounted for all sensitive customer assets—mitigating the risk of data exposure and ensuring proper handling of personal information.

Primary Risk: Data exposure of customer personal information

Relevant Regulation: GDPR (General Data Protection Regulation)

A thorough scan delivers immediate visibility, laying the foundation for automated policy enforcement and ongoing compliance.

Prerequisites

Permissions & Roles

  • Snowflake ACCOUNTADMIN or SYSADMIN role
  • USAGE privileges on databases and schemas
  • SELECT privileges on tables and views

External Tools

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

Prior Setup

  • Snowflake account provisioned
  • Network connectivity configured
  • Data governance framework in place
  • Classification policies defined

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 Natural Language Processing (NLP) techniques, including Named Entity Recognition (NER), Cyera automatically identifies customer data patterns in Snowflake tables, ensuring you stay ahead of data exposure risks and meet GDPR requirements in real time.

Step-by-Step Guide

1
Configure your Snowflake connection

Set up a service account with appropriate read-only permissions across all databases containing customer data. Configure network access rules for secure connectivity.

CREATE ROLE CYERA_READER;
GRANT USAGE ON WAREHOUSE COMPUTE_WH TO ROLE CYERA_READER;

2
Enable automated discovery workflows

In the Cyera portal, navigate to Integrations → DSPM → Add new. Select Snowflake, provide your account URL and service account credentials, then configure the discovery scope to include customer-facing databases.

3
Configure classification rules

Set up customer data classification policies using Cyera's AI-powered detection engines. Define patterns for customer names, addresses, phone numbers, and email addresses using both regex and NER models.

4
Review and validate findings

Examine the initial detection report, prioritize tables with high-volume customer data, and fine-tune classification rules to reduce false positives. Set up recurring scans to maintain continuous visibility.

Architecture & Workflow

Snowflake Information Schema

Source of metadata for databases, schemas, and tables

Cyera Connector

Queries metadata and samples data for AI-powered classification

Cyera AI Engine

Applies NER models and pattern matching for customer data detection

Compliance Dashboard

GDPR-focused reporting and remediation workflows

Data Flow Summary

Enumerate Databases → Sample Data → Apply AI Classification → Generate Compliance Report

Best Practices & Tips

Performance Considerations

  • Use Snowflake's SAMPLE function for large tables
  • Schedule scans during off-peak hours
  • Leverage clustering keys for efficient sampling

Classification Accuracy

  • Train models on your specific customer data formats
  • Use column name patterns as additional signals
  • Maintain whitelists for test/synthetic data

Common Pitfalls

  • Missing transient tables and temporary views
  • Overlooking shared databases from Data Marketplace
  • Not accounting for semi-structured JSON columns

References & Further Reading

Next Steps

🛡️ Prevent: Set up access controls for customer data 🔧 Fix: Remediate exposed customer data tables