Data Masking for PostgreSQL

Introduction

In the ever-evolving landscape of data management, safeguarding sensitive information has become paramount. One of the popular techniques is data masking, a process that obscures original data with fictional or pseudonymous data. For PostgreSQL, there are a couple of options for masking data.

This blog post delves into the why, how, and the differences between static and dynamic data masking. We will explore the available options, including PostgreSQL Anonymizer and Baffle Data Protection for Databases.

Static vs. Dynamic Data Masking

Understanding the nuances between static and dynamic data masking is essential for crafting an effective data protection strategy. Static data masking involves permanent alteration of data which is helpful when creating copies of production data for use in development and testing, or when sharing data with partners.

Dynamic masking occurs in real-time, using Role-Based Access Control (RBAC) policies to determine what a user sees and what is masked. For example, a support specialist may only view the last four digits of a credit card number, but a payment application can access the full number in clear text.

Data Masking Options for PostgreSQL

PostgreSQL Anonymizer

PostgreSQL Anonymizer is an extension available as an open source tool for PostgreSQL databases. It uses a declarative approach where data masking rules are declared using the PostgreSQL Data Definition Language.

Advantages:

• Rich Functionality:
  • Offers a plethora of masking functions such as substitution, randomization, faking, pseudonymization, partial scrambling, shuffling, noise addition, and generalization, providing flexibility in implementation.
• Static plus Dynamic Data Masking:
  • Supports both static and dynamic data masking, allowing organizations to choose the most suitable approach for their requirements.
• Open Source:
  • Being an open-source solution, PostgreSQL Anonymizer is freely available, making it an accessible option for organizations with budget constraints.

Challenges:

• Complexity:
  • Configuring and using PostgreSQL Anonymizer can be challenging, and requires writing declarative statements with an intimate knowledge of the database systems.
• Performance Impact:
  • Dynamic masking can significantly impact database query performance, potentially leading to latency issues due to real-time anonymization processes.
• Disparate Tools:
  • You may need to use different tools for encryption, tokenization, key management, etc., introducing complexity in the overall data protection strategy.
  • If your organization is also using other database systems such as MySQL or Amazon Redshift, you will not be able to centralize your policy management and enforcement.
• Access Control Challenges:
  • Administrators at both the cloud and database levels still have access to sensitive data, posing challenges for compliance and security requirements.

Baffle Data Protection for Databases

Baffle uses a proxy-based approach that provides on-the-fly data anonymization without requiring any application code changes. It is centrally managed using a visual management GUI that can span across multiple database types and instances.

Ease of Use

• Requires no code changes to applications, and uses a graphical interface to define and manage policies.

Performance Optimization

• No perceptible impact on application performance, ensuring that data protection does not come at the cost of operational efficiency.

Integrated Platform

• Offers a comprehensive suite of data protection capabilities including data masking, encryption, tokenization, and more. This unified approach streamlines management and enhances efficiency.

Centralized Policy Enforcement

• Create policies once in the central management GUI and apply everywhere sensitive data is stored.

Role-Based Access Control

• Specify dynamic data masking rules by user role, protecting data against insider threat and account takeover breaches from even database and cloud administrator accounts.

Heterogeneous

• Works across multiple database types, both on-premises and in the cloud. It seamlessly integrates with popular key management systems, ensuring compatibility in diverse environments.

Automated Bulk Policy Creation

• Define and deploy thousands of policies in minutes

Conclusion

You have choices on how you enable data masking for PostgreSQL databases. For relatively simple environments or where all you need is static data masking for a few instances, PostgreSQL Anonymizer is an excellent tool to get started with. For more complex or heterogeneous environments where performance, higher security assurance, and automation are required, Baffle Data Protection for Databases provides a proven enterprise-grade solution that is also easy to deploy and manage.

To learn more about Baffle, request a demo with one of our experts.