Snowflake Unstructured Data Detection

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

shadow data

GDPR

snowflake

Why It Matters

The core goal is to identify every location where unstructured data is stored within your Snowflake environment, so you can remediate unintended exposures before they become breaches. Scanning for unstructured data in Snowflake is a priority for organizations subject to GDPR, as it helps you prove you've discovered and accounted for all sensitive assets hidden in documents, logs, and semi-structured files—mitigating the risk of shadow data proliferation.

Primary Risk: Shadow data proliferation and uncontrolled sensitive information exposure

Relevant Regulation: GDPR General Data Protection Regulation

A thorough scan delivers immediate visibility into your unstructured data landscape, laying the foundation for automated policy enforcement and ongoing compliance.

Prerequisites

Permissions & Roles

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

External Tools

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

Prior Setup

  • Snowflake account provisioned
  • Unstructured data stages configured
  • Network policies defined
  • Authentication configured

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, Cyera automatically scans unstructured data in Snowflake—including JSON documents, text files, and embedded content—to identify hidden personal information, ensuring you stay ahead of shadow data risks and meet GDPR compliance requirements in real time.

Step-by-Step Guide

1
Configure your Snowflake environment

Ensure proper access to databases containing unstructured data and create a service account with the minimum required privileges for scanning VARIANT, OBJECT, and ARRAY columns.

snowsql -a -u

2
Enable unstructured data scanning

In the Cyera portal, navigate to Integrations → DSPM → Add new. Select Snowflake, provide your account URL and service credentials, then define the scan scope to include stages, tables with VARIANT columns, and file formats.

3
Configure AI-powered classification

Enable Cyera's NLP models to parse unstructured content, extract entities, and apply semantic classification. Configure custom patterns for organization-specific data types and adjust confidence thresholds for optimal accuracy.

4
Validate results and establish monitoring

Review the initial detection report, prioritize findings with high-confidence personal data matches, and set up continuous monitoring to catch new unstructured data ingestion. Create alerts for GDPR-relevant data types.

Architecture & Workflow

Snowflake Stages & Tables

Source of unstructured files and VARIANT data

Cyera AI Scanner

NLP-powered content analysis and entity extraction

Classification Engine

Applies ML models and semantic understanding

Risk & Compliance Hub

GDPR reporting and remediation workflows

Data Flow Summary

Scan Stages & Variants → Extract & Parse Content → Apply NLP Classification → Generate Risk Reports

Best Practices & Tips

Performance Considerations

  • Start with small sample sizes for large files
  • Use warehouse scaling for intensive scans
  • Schedule scans during off-peak hours

Tuning NLP Detection

  • Adjust confidence thresholds for text analysis
  • Create custom patterns for domain-specific data
  • Fine-tune entity recognition models

Common Pitfalls

  • Missing external stages with sensitive files
  • Over-scanning compressed archives without sampling
  • Ignoring nested JSON structures in VARIANT columns

References & Further Reading

Next Steps

🛡️ Prevent: Implement access controls for unstructured data 🔧 Fix: Remediate exposed unstructured data findings