Fortifying Data Security – Static and Dynamic Masking Policies Across Snowflake Environments

About the Client:

A well-established regional bank with multi-state presence offering retail banking, commercial lending, investment services, and digital banking products. The bank manages large volumes of sensitive customer data and operates under strict regulatory oversight.

Background:

The bank had centralized analytical and operational data on Snowflake. With increasing internal access requirements across teams—marketing, compliance, risk, development—traditional access controls became inadequate in protecting sensitive data such as PII and financial details.

Challenge:

Admin overhead in maintaining multiple access rules and custom views.

Needed fine-grained access control at column level based on user roles.

Compliance with GLBA, GDPR, CCPA, and PCI DSS required strict data masking and audit capabilities.

Risk of over-privileged access and manual view creation led to duplication and inconsistency.

Developers required production-like data for QA, without exposing real PII.

Solution:

To overcome these hurdles, we designed and implemented a modern, scalable data foundation using Snowflake as the core platform, structured around a Medallion Architecture.

Static and Dynamic Data Masking with Snowflake

  • Sensitive Data Discovery: Leveraged Snowflake’s automatic data classification to identify and tag sensitive columns like SSN, EMAIL, ACCOUNT_NUMBER, and DOB.
  • Persona-Based Design: Visibility rules defined per persona (e.g., MARKETING_ANALYST, RISK_MODELER, COMPLIANCE_OFFICER).
  • Masking Rules:
    • Static Masking: Applied in dev/QA environments—PII permanently tokenized.
    • Dynamic Masking: Applied in production—Snowflake MASKING POLICIES used SQL logic to conditionally mask data at query time.
      • e.g., SSNs masked as ***-**-****, account numbers partially revealed, emails hashed, fields nulled for unauthorized roles.
  • Policy Deployment:
    • Policies bound to Snowflake roles with automated CI/CD deployment across environments.
    • Leveraged Snowflake’s ACCESS_HISTORY and QUERY_HISTORY for audit tracking.

Outcome:

  • Masking enforced uniformly across roles and environments, reducing risk of data exposure
  • Automated policies simplified audits and ensured consistent compliance with regulatory requirements
  • Developers accessed masked, production-like data safely, cutting provisioning delays by 30%
  • Centralized column-level policy management eliminated manual view creation
  • Secure data access increased user adoption and boosted confidence in the platform

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