GCP Analytics Data Detection

Learn how to detect analytics data in Google Cloud Platform environments. Follow step-by-step guidance for GDPR compliance.

shadow data

GDPR

gcp

Why It Matters

The core goal is to identify every location where analytics data is stored within your Google Cloud Platform environment, so you can remediate unintended exposures before they become breaches. Scanning for analytics data in GCP is a priority for organizations subject to GDPR, as it helps you prove you've discovered and accounted for all sensitive analytical assets—mitigating the risk of shadow data repositories operating outside your governance framework.

Primary Risk: Shadow data repositories containing sensitive analytics

Relevant Regulation: General Data Protection Regulation (GDPR)

A thorough scan delivers immediate visibility across BigQuery, Cloud Storage, and other analytics services, laying the foundation for automated policy enforcement and ongoing compliance.

Prerequisites

Permissions & Roles

  • BigQuery Data Viewer or Admin role
  • Cloud Storage Object Viewer permissions
  • Sensitive Data Protection Admin role

External Tools

  • Google Cloud CLI (gcloud)
  • Cyera DSPM account
  • Service account credentials

Prior Setup

  • GCP project with billing enabled
  • BigQuery datasets provisioned
  • Cloud Storage buckets configured
  • API access 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 Natural Language Processing (NLP) techniques, Cyera automatically identifies analytics data patterns in BigQuery tables, Cloud Storage files, and Dataflow pipelines, ensuring comprehensive coverage of your GCP analytics ecosystem while maintaining GDPR compliance requirements.

Step-by-Step Guide

1
Configure GCP service account

Create a service account with the minimum required privileges for BigQuery and Cloud Storage access. Enable necessary APIs and generate JSON credentials.

gcloud iam service-accounts create cyera-scanner --display-name="Cyera Analytics Scanner"

2
Enable Cyera GCP integration

In the Cyera portal, navigate to Integrations → Cloud Platforms → Add GCP. Upload your service account JSON, specify project IDs, and define the scope for BigQuery datasets and Cloud Storage buckets to scan.

3
Configure analytics data detection rules

Set up custom detection patterns for analytics data types including user behavior data, performance metrics, business intelligence reports, and aggregated datasets. Enable AI-powered content analysis for unstructured analytics files.

4
Run discovery scan and review findings

Execute the initial scan across your GCP environment. Review detected analytics datasets, validate classifications, and prioritize remediation based on data sensitivity and access patterns. Configure automated monitoring for ongoing visibility.

Architecture & Workflow

BigQuery Information Schema

Source of metadata for datasets and tables

Cloud Storage API

Discovers analytics files and objects

Cyera AI Engine

Applies NLP and pattern recognition for classification

GDPR Compliance Dashboard

Reports, audit trails, and remediation workflows

Data Flow Summary

Enumerate Resources → Sample & Classify → Apply AI Detection → Generate Findings

Best Practices & Tips

Performance Considerations

  • Start with smaller datasets and scale incrementally
  • Use BigQuery slots efficiently during scans
  • Schedule scans during off-peak hours

Detection Accuracy

  • Train models on your specific analytics patterns
  • Maintain glossaries of business-specific terms
  • Regularly update classification rules

Common Pitfalls

  • Missing cross-project BigQuery views
  • Overlooking temporary Cloud Storage objects
  • Ignoring Dataflow streaming job outputs

References & Further Reading

Next Steps

🛡️ Prevent: Set up proactive analytics data protection 🔧 Fix: Remediate exposed analytics datasets