Snowflake Analytics Data Exposure Remediation

Why It Matters

The core goal is to remediate exposed analytics data within your Snowflake environment, ensuring that sensitive insights and aggregated information are properly secured against unauthorized access. Fixing analytics data exposure in Snowflake is critical for organizations subject to GDPR regulations, as it helps prevent data breaches and ensures compliance with data protection requirements—mitigating the risk of unauthorized access to business intelligence and analytical datasets.

Primary Risk: Data exposure of sensitive analytics and business intelligence

Relevant Regulation: GDPR Data Protection Regulation

Proper remediation delivers immediate security improvements, establishing robust access controls and ongoing protection for your analytical assets.

Prerequisites

Permissions & Roles

Snowflake ACCOUNTADMIN or SECURITYADMIN role
MANAGE GRANTS privilege on schemas and tables
Ability to create and modify security policies

External Tools

Snowflake CLI or SnowSQL
Cyera DSPM account
API credentials

Prior Setup

Snowflake account provisioned
Data governance framework established
Exposed analytics data identified
Network 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 automatically identifies exposed analytics data in Snowflake, analyzes data relationships and usage patterns, and provides intelligent remediation recommendations to secure your business intelligence assets in real time.

Step-by-Step Guide

Review exposed analytics data findings

Analyze the exposure report to understand which analytics tables, views, and dashboards have inappropriate access permissions or lack proper security controls.

SELECT * FROM INFORMATION_SCHEMA.TABLE_PRIVILEGES WHERE GRANTEE = 'PUBLIC';

Implement row-level security policies

Create and apply row access policies to sensitive analytics data. Configure dynamic data masking for columns containing sensitive information in analytical datasets.

CREATE ROW ACCESS POLICY analytics_access_policy AS (user_role VARCHAR) RETURNS BOOLEAN ->
CASE WHEN CURRENT_ROLE() IN ('ANALYST_ROLE', 'MANAGER_ROLE') THEN TRUE ELSE FALSE END;

Revoke excessive permissions

Remove PUBLIC access and overly broad grants from analytics tables. Implement least-privilege access using custom roles and secure views for analytical workloads.

REVOKE ALL ON SCHEMA analytics_schema FROM ROLE PUBLIC;
GRANT USAGE ON SCHEMA analytics_schema TO ROLE ANALYST_ROLE;

Enable monitoring and alerts

Set up continuous monitoring through Cyera to track access patterns and data usage. Configure alerts for unauthorized access attempts to analytics data and establish automated remediation workflows.

Architecture & Workflow

Snowflake Information Schema

Source of metadata for permissions and access patterns

Cyera AI Engine

Analyzes data usage patterns and identifies exposure risks

Security Policy Engine

Applies row-level security and masking policies

Monitoring & Alerting

Continuous monitoring and automated response

Remediation Flow Summary

Analyze Exposure → Apply Policies → Revoke Access → Monitor Compliance

Best Practices & Tips

Access Control Strategy

Implement role-based access with minimal privileges
Use secure views for analytics workloads
Regular access reviews and cleanup

Data Masking Techniques

Apply dynamic masking to sensitive columns
Use conditional masking based on user roles
Maintain data utility for analytics while protecting privacy

Common Pitfalls

Over-restrictive policies breaking analytics workflows
Forgetting to update policies when data schemas change
Not considering data lineage in analytical datasets

References & Further Reading

Next Steps

🛡️ Prevent: Implement proactive analytics data protection 🔍 Detect: Set up continuous analytics data monitoring