Calendar Icon White
December 28, 2023
Clock Icon
 min read

Bulk Data Masking for Databases like PostgreSQL

Learn about static and dynamic data masking for large amount of data in databases like PostgreSQL. Discover the capabilities of bulk data masking features of Strac.

Bulk Data Masking for Databases like PostgreSQL
Calendar Icon White
December 28, 2023
Clock Icon
 min read

Bulk Data Masking for Databases like PostgreSQL

Learn about static and dynamic data masking for large amount of data in databases like PostgreSQL. Discover the capabilities of bulk data masking features of Strac.


  • The surge in sensitive data within PostgreSQL calls for the implementation of data masking.
  • Static (permanent alteration) and Dynamic (real-time, reversible) data masking can guide your security strategy.
  • The PostgreSQL Anonymizer offers flexibility in static and dynamic data masking, yet it involves a complex setup process.
  • Applying views and triggers within PostgreSQL can be a practical approach to data masking.
  • Strac offers advanced data masking techniques like redaction and format-preserving pseudonyms, along with seamless PostgreSQL integration.

PostgreSQL is known for its robustness, extensibility, and compliance with SQL standards. It has evolved into a highly respected and widely used open-source relational database system. As the PostgreSQL  becomes a repository for an ever-increasing volume of sensitive information, the need for data masking becomes apparent. 

In 2023, the worldwide average expense incurred from a data breach surged to an alarming USD 4.45 million, marking a significant 15% increase in three years. This escalating financial impact has prompted organizations to consider enhancing security measures.

This post explores data masking, its importance, and its practical implementation in safeguarding sensitive data. From the complexities of static and SQL server dynamic data masking to the advanced capabilities of tools like the PostgreSQL anonymizer, we aim to help you secure your database.

What is Data Masking and How Does it Work in Databases?

Data masking is a vital process in database management, particularly in PostgreSQL, where it safeguards sensitive information. This technique involves replacing real data with realistic but altered content, ensuring that sensitive details like personal identifiers or financial records remain secure, especially during development, testing, or analytics.

The process, a key part of how data masking works, begins with identifying sensitive data that needs protection. Specific rules are then applied to this data, using various techniques like substitution, shuffling, or tokenization. These methods effectively obscure the original data while maintaining its usability for operational purposes.

The Role of Data Masking in Safeguarding Sensitive Data

Understanding the benefits of data masking in databases is crucial for maintaining security and privacy. 

Ensuring data privacy in large datasets

Data masking acts as a protective barrier, shielding sensitive data from unauthorized access. It ensures that sensitive information is transformed into an unreadable format for unauthorized users while retaining its usability for legitimate purposes, thereby securing large datasets effectively.

Real-time protection and accessibility

Data masking is renowned for providing real-time protection of sensitive data. It can permanently as well as temporarily mask data at the point of access. This method is particularly advantageous in scenarios requiring frequent but secure data access, such as in customer service databases or financial records.

Compliance and Security

Beyond safeguarding privacy, data masking is instrumental in complying with stringent privacy regulations such as GDPR. By restricting sensitive data access to authorized individuals only, organizations can mitigate the risks of data breaches and the associated non-compliance penalties.

Static vs. Dynamic Data Masking

Both Static and Dynamic masking methods offer unique advantages and are tailored to specific scenarios. To provide a clearer understanding of these two approaches, let's compare their key features, use cases, benefits, and suitability below:

Feature Static Data Masking Dynamic Data Masking
Definition Permanently alters data, making it irreversible. Temporarily masks data in real-time, without altering the underlying data.
Use Cases Ideal for development, testing, and sharing data externally. Suited for production environments and customer service databases.
Benefits High level of security as data is completely obscured. Balances data security with usability by keeping original data intact.
Suitability for Datasets Suitable for small datasets due to its permanent nature. Highly effective for large datasets requiring frequent and secure access.
PostgreSQL Environments Recommended for non-production environments. Preferred in production environments with large, sensitive datasets.

Bulk Data Masking with the PostgreSQL Anonymizer Extension

PostgreSQL anonymizer, an open-source extension, is designed to anonymize or mask sensitive data within PostgreSQL databases. It's a response to the growing need for data privacy and security, especially in environments handling personal identification information or commercially sensitive data. 

A key feature of PostgreSQL Anonymizer is its support for both static and dynamic masking. Static data masking permanently alters the data, making it suitable for scenarios like development and testing environments where the original data is not required. Dynamic masking, on the other hand, is applied in real-time and is ideal for production environments where data needs to be accessed but not exposed

Despite its advantages, using PostgreSQL Anonymizer comes with challenges. The complexity of setting up and configuring the extension can be daunting, particularly for those not deeply familiar with PostgreSQL's architecture. It requires a detailed understanding of the database schema and the specific data that needs masking. Additionally, SQL server dynamic data masking can impact database performance, especially with large datasets or complex queries.

Practical implementation of data masking in PostgreSQL

Two key methods for data masking in PostgreSQL are through the use of views for dynamic data masking and the utilization of triggers for more complex masking scenarios.

Dynamic Data Masking with PostgreSQL Views

Views act as virtual tables, presenting data differently than it is stored in the database. They can be used to mask sensitive data dynamically, meaning the data appears masked when the view is queried, but the underlying data in the database remains unchanged.

To implement dynamic data masking, you create a view that selects data from the original table but with certain sensitive columns masked. 

For example, a view could show only the last four digits of a Social Security number or a customer's name replaced with a pseudonym. This method is particularly useful for scenarios where users need to access data in a readable format but without exposing sensitive details.

Utilizing Triggers in PostgreSQL for Data Masking

Triggers in PostgreSQL offer another layer of data masking, especially useful for more complex scenarios. A trigger is a defined function that automatically executes or fires when certain database operations occur, such as INSERT, UPDATE, or DELETE.

You can set up triggers to automatically mask data as it's entered or updated in the database. 

For instance, a trigger could be set to automatically replace inputted credit card numbers with a tokenized version or to scramble personal identifiers as they are inserted into the database. This approach ensures that sensitive data is masked right from the point of entry, enhancing data security.

How to Choose a Data Masking Tool for Databases?

Selecting the right data masking tool for databases impacts both the security and functionality of your data management system. Here's how to navigate this choice.

Selecting the right data masking tool for databases impacts both the security and functionality of your data management system. Here's how to navigate this choice.

  • Assess Your Data Masking Needs: Begin by assessing the type of data you handle. Is it personal identifiable information (PII), financial data, or other sensitive information? Understanding the nature of your data will guide you in choosing a tool that best fits your masking requirements. 
  • Adhere to Compliance Requirements: Consider any legal and compliance requirements that apply to your data. For instance, A DLP tool like Strac is designed to meet various compliance standards for organizations under stringent data protection regulations.
  • Evaluate Tool Capabilities: Determine whether you need static, dynamic, or both types of data masking. Also, evaluate how well the tool integrates with your existing database infrastructure.
  • Consider Scalability and Performance: If you're dealing with large volumes of data, consider the performance and scalability.
  • Check Customization and Control: The flexibility to tailor data masking processes to your specific needs is crucial for effective data management. Look for tools that offer customizable masking rules and policies.
  • Community and Resources: A well-supported tool with an active community and extensive documentation can significantly ease the implementation and troubleshooting process, enhancing your understanding of what is data masking.

How Strac Makes Bulk Data Masking Effortless for PostgreSQL?

Strac emerges as a versatile DLP solution, making data masking in PostgreSQL environments effortless and more efficient. Unlike the PostgreSQL anonymizer extension, Strac operates independently of PostgreSQL views and users, offering a more flexible and comprehensive approach to data masking.

Addressing the data masking challenge

There are many risks associated with sensitive data like personal identification numbers, passwords, and credit card numbers. Strac not only protects sensitive information but also ensures adherence to stringent compliance standards such as HIPAA, PCI DSS, and GDPR, thereby providing overall data security.

Multiple data masking techniques

Strac employs diverse data masking techniques to suit various data protection requirements:

  • Redaction: Strac utilizes redaction to obscure sensitive data, replacing it with symbols or other characters. This method is particularly effective for safeguarding data like credit card numbers or personal IDs, ensuring they remain confidential and secure.
  • Format-Preserving Pseudonyms: Strac also provides format-preserving pseudonyms, which substitute original data with synthetic identifiers while maintaining the data's format. This approach is useful for anonymizing names or other personal details without altering the data structure.
  • Selective Masking: For scenarios requiring partial data visibility, Strac's selective masking is ideal. It can obscure specific parts of data, such as partially masking email addresses, thus achieving a balance between data usability and privacy.
Database before masking sensitive data

To illustrate these techniques, consider the above database table with fields like user_id, name, email, company_name, and phone. Strac's approach would be as follows:

User ID: The user_id field can be left unchanged, maintaining its original value post-redaction.

Name: Names can be replaced with pseudonyms, ensuring the data remains format-consistent but anonymized.

Email: The username part of the email addresses can be masked, while the domain name is kept intact, striking a balance between privacy and usability.

Company Name: For company names, Strac can preserve the first character and mask the rest, maintaining length consistency.

Phone: Phone numbers can be tokenized, replacing them with a token that secures the original data.

Here's a glimpse of how the table appears after Strac's masking.

Database after masking sensitive data

Seamless Integration and Configuration with PostgreSQL

Strac is engineered to connect effortlessly with PostgreSQL database instances. This compatibility is crucial for organizations looking to implement data masking without the need for extensive modifications to their existing database setup. Moreover, its configuration process is straightforward, making it accessible even to those who may not have in-depth technical expertise in database management.

Importantly, Strac's integration with PostgreSQL is designed to have minimal impact on the database performance. This aspect is particularly crucial for large-scale databases where any additional load can significantly affect efficiency and response times.

Book a demo today and see data masking in action.

Founding Engineer. Ex-Amazon Payments Security Engineer for 10 years.

Latest articles

Browse all