Snowflake PII Data Detection
Learn how to detect PII in Snowflake environments. Follow step-by-step guidance for GDPR compliance using AI-powered detection.
Why It Matters
The core goal is to identify every location where personally identifiable information (PII) is stored within your Snowflake environment, so you can remediate unintended exposures before they become breaches. Scanning for PII in Snowflake is a priority for organizations subject to GDPR, as it helps you prove you've discovered and accounted for all personal data—mitigating the risk of data exposure and hefty regulatory fines.
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
- SnowSQL CLI or Snowflake Web UI
- Cyera DSPM account
- API credentials
Prior Setup
- Snowflake account provisioned
- Network policies configured
- Service account created
- Access controls 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 AI-powered Named Entity Recognition (NER) models, Cyera automatically identifies PII patterns in Snowflake tables—from email addresses and phone numbers to national identifiers—ensuring you stay ahead of accidental exposures and meet GDPR audit requirements in real time.
Step-by-Step Guide
Create a dedicated service account with minimum required privileges for data discovery. Enable automatic sensitive data classification if using Snowflake's built-in features.
In the Cyera portal, navigate to Integrations → DSPM → Add new. Select Snowflake, provide your account URL and service account credentials, then define the scan scope including specific databases and schemas.
Configure webhooks or streaming exports to push scan results into your SIEM or Security Hub. Link PII findings to existing ticketing systems like Jira or ServiceNow for remediation tracking.
Review the initial detection report, prioritize tables with large volumes of PII, and adjust detection rules to reduce false positives. Schedule recurring scans to maintain visibility as data evolves.
Architecture & Workflow
Snowflake Information Schema
Source of metadata for tables and columns
Cyera Connector
Pulls metadata and samples data for classification
AI Classification Engine
Applies NER models and PII detection algorithms
Reporting & Remediation
Dashboards, alerts, and compliance reports
Data Flow Summary
Best Practices & Tips
Performance Considerations
- Start with incremental or scoped scans
- Use sampling for very large tables
- Schedule scans during off-peak hours
Tuning Detection Rules
- Maintain allowlists for test/synthetic data
- Adjust confidence thresholds for PII types
- Configure region-specific PII patterns
Common Pitfalls
- Forgetting shared databases from data marketplace
- Over-scanning transient or temporary tables
- Neglecting to rotate service account credentials