Snowflake Dynamic Data Masking (DDM) and Alternatives

Snowflake Dynamic Data Masking#

Snowflake Dynamic Data Masking (DDM) is a security feature that allows you to mask sensitive data in real-time without creating separate masked copies of your data. It enables organizations to protect personally identifiable information (PII) and other sensitive data while maintaining data utility for authorized users.

Dynamic data masking works by applying masking policies at the column level, transforming data on-the-fly based on the user's role and permissions. This approach ensures that sensitive data remains protected while allowing different levels of data access for different human users and services.

Here's how to create and apply a simple masking policy in Snowflake:

-- Create a masking policy for email addresses
CREATE OR REPLACE MASKING POLICY email_mask AS (val STRING)
RETURNS STRING ->
  CASE
    WHEN CURRENT_ROLE() IN ('ADMIN', 'DATA_ANALYST') THEN val
    ELSE REGEXP_REPLACE(val, '.+@', '*****@')
  END;

-- Apply the masking policy to a column
ALTER TABLE customers
MODIFY COLUMN email SET MASKING POLICY email_mask;

Limitations and Challenges#

While Snowflake Dynamic Data Masking provides powerful security capabilities, there are several important considerations and limitations to be aware of:

1. Enterprise Edition Requirement and Cost Impact#

Snowflake DDM, which is part of Column-level security, is only available in the Enterprise edition and higher, which significantly impacts pricing:

For organizations currently on Standard edition, enabling DDM requires upgrading to Enterprise, resulting in a 50% increase in compute costs across all workloads. This cost overhead applies to your entire Snowflake usage, not just masked data operations.

2. Policy Management Complexity#

Managing Snowflake DDM at scale presents significant operational challenges due to policy proliferation across sensitive columns, lack of proper processes and audit trails to track policy changes, and the absence of Snowsight UI support - forcing teams to manage complex masking policies entirely through SQL commands, which becomes increasingly difficult to maintain and govern as the number of policies grows.

-- Example of policy complexity with multiple roles and conditions
CREATE OR REPLACE MASKING POLICY customer_pii_mask AS (val STRING)
RETURNS STRING ->
  CASE
    WHEN CURRENT_ROLE() = 'DATA_OWNER' THEN val
    WHEN CURRENT_ROLE() = 'ANALYST_SENIOR' AND
         CURRENT_WAREHOUSE() = 'ANALYTICS_WH' THEN val
    WHEN CURRENT_ROLE() = 'CUSTOMER_SERVICE' AND
         CURRENT_TIME() BETWEEN '09:00'::TIME AND '17:00'::TIME THEN
         CONCAT(LEFT(val, 3), '***', RIGHT(val, 2))
    WHEN CURRENT_ROLE() IN ('ANALYST_JUNIOR', 'INTERN') THEN '***MASKED***'
    ELSE NULL
  END;

Alternatives to Snowflake Dynamic Data Masking#

Given the limitations and costs associated with Snowflake DDM, organizations often explore alternative approaches to protect sensitive data:

1. Database Views#

Database views provide a cost-effective way to implement data masking without requiring Enterprise edition. Views can incorporate role-based logic and masking functions to protect sensitive data at the query level.

-- Create a masked view for customer data
CREATE OR REPLACE VIEW customers_masked AS
SELECT
    customer_id,
    customer_name,
    CASE
        WHEN CURRENT_ROLE() IN ('ADMIN', 'DATA_ANALYST')
        THEN email
        ELSE REGEXP_REPLACE(email, '.+@', '*****@')
    END AS email,
    CASE
        WHEN CURRENT_ROLE() = 'FINANCE_ADMIN'
        THEN credit_card_number
        WHEN CURRENT_ROLE() = 'CUSTOMER_SERVICE'
        THEN CONCAT('****-****-****-', RIGHT(credit_card_number, 4))
        ELSE '****-****-****-****'
    END AS credit_card_number,
    registration_date
FROM customers;

• Pros: No additional license required as it works with all Snowflake editions.
• Cons: Even more complex to manage than masking policies due to view proliferation and lack of enforcement.

2. Bytebase#

Bytebase is a database DevSecOps platform that provides dynamic data masking capabilities across multiple database systems, including Snowflake.

How Bytebase DDM Works#

Middleware Architecture

Unlike Snowflake's native DDM, Bytebase doesn't rely on Snowflake's data masking features and operates as a middleware layer between users and Snowflake, intercepting all database queries and applying masking rules before returning results to the user.

Policy Configuration

Users configure masking policies through Bytebase's web UI for intuitive management, or programmatically via Terraform Provider and REST API for automation and infrastructure-as-code workflows.

Query Execution

When users query data through Bytebase's SQL Editor, the platform automatically applies the configured masking policies in real-time, ensuring sensitive data is protected without requiring changes to the underlying database structure.

Pros:

• Fractional cost at typically 10% of upgrading to Snowflake Enterprise edition.
• UI-based masking policy configuration and grant masking exemptions management, with support for Terraform Provider and API integration.
• Provides the same workflow to configure masking policies across different database systems beyond just Snowflake.

Cons:

• Masking is only enforced when users query data through Bytebase's SQL Editor.
• Only covers human-to-database path and does not enforce service-to-database connections.

Comparison#

While Snowflake's native DDM provides the most comprehensive coverage, organizations looking to balance cost, manageability, and protection for human access patterns may find Bytebase offers the best compromise for their data masking needs.