GCP Unstructured Data Detection

Learn how to detect unstructured 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 unstructured data is stored within your Google Cloud Platform environment, so you can remediate unintended exposures before they become breaches. Scanning for unstructured data in GCP is a priority for organizations subject to GDPR, as it helps you prove you've discovered and accounted for all sensitive assets—mitigating the risk of shadow data spreading across your cloud infrastructure.

Primary Risk: Shadow data proliferation across cloud services

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

  • Cloud Storage Admin or service account
  • storage.objects.list, storage.objects.get privileges
  • Ability to install gcloud CLI or Terraform

External Tools

  • Google Cloud CLI
  • Cyera DSPM account
  • API credentials

Prior Setup

  • GCP project provisioned
  • Cloud Storage buckets enabled
  • CLI authenticated
  • IAM policies 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 can analyze unstructured data in GCP—including documents, images, and free-form text—to identify sensitive content patterns and ensure you stay ahead of accidental exposures while meeting GDPR audit requirements in real time.

Step-by-Step Guide

1
Configure your GCP project

Ensure Cloud Storage API is enabled in your project and create a service account with the minimum required privileges for bucket enumeration and object scanning.

gcloud auth login

2
Enable scanning workflows

In the Cyera portal, navigate to Integrations → DSPM → Add new. Select Google Cloud Platform, provide your project ID and service account details, then define the scan scope for Cloud Storage buckets.

3
Integrate with third-party tools

Configure webhooks or streaming exports to push scan results into your SIEM or Security Hub. Link findings to existing ticketing systems like Jira or ServiceNow.

4
Validate results and tune policies

Review the initial detection report, prioritize buckets with large volumes of unstructured data, and adjust detection rules to reduce false positives. Schedule recurring scans to maintain visibility.

Architecture & Workflow

Google Cloud Storage

Source of unstructured files and documents

Cyera Connector

Pulls metadata and samples content for classification

Cyera AI Engine

Applies NLP models and content analysis

Reporting & Remediation

Dashboards, alerts, and playbooks

Data Flow Summary

Enumerate Buckets → Send to Cyera → Apply NLP Detection → Route Findings

Best Practices & Tips

Performance Considerations

  • Start with incremental or scoped scans
  • Use sampling for very large file repositories
  • Tune sample rates for speed vs coverage

Tuning Detection Rules

  • Maintain allowlists for synthetic datasets
  • Adjust confidence thresholds for NLP models
  • Match rules to your risk tolerance

Common Pitfalls

  • Forgetting archived or lifecycle-managed objects
  • Over-scanning temporary or staging buckets
  • Neglecting to rotate service account credentials

References & Further Reading

Next Steps

🛡️ Prevent: Set up access controls for unstructured data 🔧 Fix: Review and remediate exposed unstructured files