Dynamic Data Masking for ScyllaDB

Introduction

In an era where data breaches cost enterprises millions annually, safeguarding sensitive information is non-negotiable. Dynamic Data Masking (DDM) has emerged as a critical technique to protect sensitive data while maintaining usability. Unlike static masking, which permanently alters data, DDM obscures sensitive fields in real-time based on user roles, ensuring that only authorized personnel view unmasked data. For high-performance databases like ScyllaDB, implementing DDM is challenging due to its lack of native support. However, innovative solutions like proxy-based masking and third-party tools such as DataSunrise bridge this gap effectively.

This article explores how to implement dynamic data masking in ScyllaDB using both native workarounds and advanced tools. We’ll cover practical examples, including CQL functions and DataSunrise’s enterprise-grade features, to help you secure sensitive data without compromising performance.

What is Dynamic Data Masking?

Dynamic Data Masking (DDM) is a security measure that hides sensitive data in real-time during query execution. For instance, a customer support agent might see only the last four digits of a credit card number, while a database administrator views the full value. This minimizes exposure risks without altering the underlying data.

In ScyllaDB, a NoSQL database optimized for speed and scalability, DDM isn’t natively supported. Organizations must therefore rely on external tools or custom application logic to mask data dynamically. Common use cases include:

• Compliance: Meeting regulations like GDPR or HIPAA.
• Role-Based Access: Restricting data visibility based on user roles.
• Testing Environments: Sharing masked production data with developers.

Native Dynamic Data Masking for Scylla

While ScyllaDB lacks built-in DDM, limited masking can be achieved through CQL User-Defined Functions (UDFs). These functions allow you to define custom logic for obfuscating data directly within your queries. Below, we explore how to use UDFs for basic dynamic data masking.

Example: Masking Email Addresses

To mask email addresses dynamically, you can create a UDF that replaces part of the email with asterisks. Here’s how:

CREATE FUNCTION mask_email(email TEXT) 
RETURNS NULL ON NULL INPUT 
RETURNS TEXT 
LANGUAGE lua AS $$
  local user, domain = string.match(email, "([^@]+)@(.+)")
  return string.sub(user, 1, 1) .. "***@" .. domain
$$;

Once the function is created, you can use it in your queries:

SELECT mask_email(email) FROM users WHERE id = 101;

Limitations of Native UDFs

While UDFs provide a straightforward way to implement basic masking, they come with limitations:

• Role-Based Policies: UDFs cannot enforce role-based masking natively.
• Performance Overhead: Complex masking logic can impact query performance.
• Limited Flexibility: UDFs are not ideal for advanced masking scenarios, such as conditional masking based on user roles or IP addresses.

For more robust and scalable solutions, third-party tools like DataSunrise are recommended.

DataSunrise: Advanced Dynamic Data Masking

For organizations requiring enterprise-grade dynamic data masking, third-party tools like DataSunrise offer a comprehensive solution. DataSunrise acts as a proxy between the application and ScyllaDB, intercepting queries and applying masking rules in real-time. One of its standout features is the ability to mask data based on user roles, IP ranges, or devices, providing granular control over data visibility.

Role-Based Masking

DataSunrise allows you to define masking rules that apply only to specific user roles. For example, you can configure the system to reveal full email addresses to administrators while masking them for other users.

Example: Masking Credit Card Numbers

1. Create a Masking Rule:
   • Target Field: credit_card
   • Masking Pattern: XXXX-XXXX-XXXX-#### (reveals last four digits)
   • Applied Roles: All users except finance_team
2. Query Execution:
   • A marketing user’s query returns XXXX-XXXX-XXXX-1234.
   • The finance team receives the full value 4111-1111-1111-1234.

This feature ensures that sensitive data is only exposed to authorized personnel, significantly reducing the risk of data breaches.

Additional Features

While role-based masking is a standout feature, DataSunrise also offers:

• IP Range and Device-Based Masking: Restrict data visibility based on the user’s IP address or device type.
• Table/Keyspace-Level Masking: Apply masking rules to specific tables or keyspaces, ensuring that only relevant data is obfuscated.
• Query Blocking: Disconnect users or block queries that attempt to perform unauthorized UPDATE or DELETE operations.

Best Practices for Dynamic Data Masking

1. Identify Sensitive Fields: Audit your database to classify PII, financial data, and health records.
2. Least Privilege Access: Grant unmasked access only to roles that absolutely need it.
3. Audit Masking Rules: Regularly review rules to ensure they align with compliance requirements.
4. Monitor Performance: Use tools like ScyllaDB Monitoring to track latency introduced by masking.
5. Combine with Encryption: Masking isn’t a substitute for encryption—use both for layered security.

Conclusion

Dynamic Data Masking is essential for balancing data utility and security in ScyllaDB. While native workarounds like UDFs offer basic obfuscation, third-party solutions like DataSunrise provide enterprise-grade features such as role-based policies and real-time masking without sacrificing performance. By following best practices and leveraging the right tools, organizations can protect sensitive data, comply with regulations, and maintain user trust in their ScyllaDB deployments.