Data Anonymization vs. Data Masking: Why You Need Masking

Data masking vs. data anonymization: what’s the difference? 

Static data masking (commonly called data masking) and traditional data anonymization are methods for eliminating sensitive data. Sensitive data includes personally identifiable information (PII data) and personal health information (PHI).

Data masking and anonymization can both be used to secure data and ensure it complies with privacy regulations such as GDPR, DORA, and CCPA. This blog will explain each technique and how they differ from each other. Plus, it includes our advice on when to use masking vs. anonymization. Let’s get started!

Data Masking vs. Data Anonymization: Definitions

Data Masking Defined

Data masking replaces sensitive data with fictitious but realistic values. This protects the original source data while maintaining its usability. By retaining the original structure and format, you’ll be able to use data in non-production environments — without creating sensitive data risks. 

Masking preserves referential integrity between the original data and its fictitious counterpart. So, in simple terms, if "Steve" is masked as "Eric" in one system, every instance of "Steve" across all related systems will also be masked as "Eric." 

Masking is also irreversible. Masked data cannot be used to re-identify individuals or sensitive information. By masking, you’ll mitigate data risk. And you’ll ensure that masked data cannot be utilized nefariously if stolen.

What is Data Anonymization?

Traditional data anonymization is a broad category of approaches to remove or transform PII. 

Some traditional anonymization techniques include:

• Redaction: Hides or removes sensitive information from a dataset.
• Tokenization: Replaces sensitive data with a unique, randomly generated string or identifier. Tokenization can often be reversed using a “key” generated during the process.
• Summarization/aggregation: Condenses large datasets, documents, or text into shorter, more concise forms or summaries. These summaries retain the core meaning or insights. 

Each of these data anonymization techniques aims to remove PII entirely. This makes re-identification of individuals based on their data highly unlikely. But anonymization doesn’t offer the same referential integrity and irreversibility that masking provides. Furthermore, anonymized data doesn’t work well for test cases.

Data Masking vs. Data Anonymization: What's the Difference?

The main difference between data masking and data anonymization is that data anonymization is a broad category of various protective measures while data masking is a specific approach to replace sensitive data with fictitious values. 

When to Use Data Anonymization vs. Data Masking

When should you use data anonymization vs. data masking? 

The Delphix Take

“Anonymization techniques carry a lot of benefits for enterprises. Our customers are using a variety of anonymization techniques that protect data in production environments. But anonymization often won’t scale or provide adequate coverage for downstream environments like development and QA. Data virtualization and masking present the most comprehensive solution for rapidly creating downstream, compliant datasets quickly and at scale at some of the world’s most demanding institutions,” says Steve Karam, Principal Sales Engineer for Delphix.

“Masking is essential for ensuring compliance in today’s data-driven enterprises. Our customers rely on data masking to protect their test development and analytics environments. Realistic but fictitious data has proven crucial for customers to shift left in their development cycle. This allows them to catch errors and defects before they become very expensive to fix,” says David Wells, Principal Product Manager for Delphix.

Explore More: Get the complete guide to data masking methods and techniques >>

Why Masking is So Important Today

Masking is important today because sensitive data growth is exploding. In the Delphix 2024 State of Data Compliance and Security Report, 75% report that the volume of sensitive data stored in non-production environments increased over the past year. And 91% report concerns about the resulting expanded exposure footprint. 

At the same time, 86% allow compliance exceptions in non-production environments. So, it’s not a surprise that 54% experienced data breaches or theft and that 43% experienced regulatory non-compliance. 

Here at Delphix, we believe it is vital to protect sensitive data in non-production environments, and that masking is the best way to do so. 

Of those we surveyed, 66% use static data masking. As we approach 2025, we believe that percentage needs to go up if organizations wish to avoid regulatory non-compliance and data breaches. 

The State of Data Compliance and Security Report

Find out what 250 enterprise leaders are doing to protect sensitive data in non-production environments. Get your copy of the report now.

Get the Data Compliance Report

Experience the Delphix Difference with Data Masking & More

The Delphix DevOps Data Platform helps enterprises like yours eliminate data risk. At the same time, Delphix increases the quality and speed of your application development. Discover more >> What Is Delphix?

How it Works

Delphix automatically discovers sensitive data across an organization. It then replaces the data with fictitious, production-like data. Delphix uses a rich library of prebuilt and customizable algorithms. This ensures data utility and referential integrity across data sources from on-premises to cloud. 

The irreversible nature of Delphix data masking ensures that your sensitive data’s privacy is airtight. But maintaining referential integrity allows it to still be useful in your non-production environments. Our library of algorithms allows you to tailor data masking and protection to suit your organization’s needs. And Delphix’s many integrations allow it to slot in with any workflow.

Our solution effectively scales from the smallest SQL server to massive billion rows of analytical sources (like Snowflake and Databricks). We ensure rapid and automated data delivery to downstream teams — when and where they need it — to shift left and improve quality.   

Real-Life Examples

Hundreds of enterprises around the world trust Delphix for data masking and delivery. Here’s how a few of them in different industries accelerate innovation with Delphix. 

Financial Services: Boeing Employee Credit Union

Boeing Employee Credit Union (BECU) needed a solution to automate sensitive data discovery and mask data consistently. With Delphix, they masked 680 million rows in 15 hours, enabling 200+ developers to get self-service data. 

“Not only does Delphix reduce our risk footprint by masking sensitive data, but we can also give developers realistic, production-like environments." — Kyle Welsh, CISO, BECU

Read the BECU case study

Telecom: Proximus

Telecom runs 24/7 and testers need to continue testing — without being blocked waiting for data for a week. With Delphix, they refreshed 60 applications in 1 weekend. They reduced masking time by 97% and non-production storage by 85%, ultimately saving over 7 million Euros over 3 years in testing labor. 

Watch the Proximus testimonial

Insurance: Delta Dental

Delta Dental used to spend 8 weeks extracting data — and it was difficult to protect sensitive data for compliance. With Delphix, they can mask data and easily deliver it to a team of 200 developers in minutes.

Read the Delta Dental case study

Get Started

With Delphix you can achieve data compliance while accelerating the speed and quality of software development and analytics initiatives. No need to make trade-offs.

Our team of experts is here to help you with data masking and compliance. Get a no-pressure demo from one of our product specialists and see how Delphix can help your organization achieve superior data masking.

Request a compliance demo