Types of Data Masking

Data masking comes in several forms, each suited to different use cases for protecting sensitive data in non-production environments. Some common data masking techniques include:

• Static Data Masking: Permanently replaces sensitive data at the source with fictitious yet realistic values. Often used for development, testing, or analytics databases where true data must not be exposed.
• Dynamic Data Masking: Masks data in real-time as it is accessed. Unauthorized users only see redacted or masked values. The underlying data remains unchanged.
• On-the-Fly Masking: Transfers masked data from production to non-production systems during the data replication process, creating secure environments without altering the original source.
• Obfuscation: Transforms sensitive information into a non-recognizable format while preserving the structure. Makes data unusable for attackers yet suitable for certain test scenarios.
• Redaction: Removes or hides sensitive parts of data, replacing them with characters like “X” or “*”. Ideal for document sharing or end-user displays.
• Scrambling: Reorders or shuffles data elements, such as characters or rows, to obscure true values. Keeps the same length and format for compatibility during software testing.
• Tokenization: Substitutes sensitive data with unique tokens that reference the original data. Typically stored securely in a separate location, allowing safe data sharing.

📘Further reading: Data Masking Methods & Techniques: The Complete Guide

Requirements Your Data Masking Solution Should Fulfill 

In working with large-scale data environments, a handful of key requirements consistently make the difference between a masking solution that simply exists and one that actually enables secure, usable data at scale.

1. Referential Integrity

Referential integrity is critical. Without it, masked data becomes fragmented across systems.

For example, George always gets masked to Elliot consistently across environments valuating, updating, and manipulating datasets in an environment — as well as across multiple datasets. (For example, it preserves referential integrity when you mask data in an Oracle database and a SQL Server database). 

In one case with a global retailer, inconsistencies between masked customer IDs across platforms rendered analytics nearly useless until a solution was implemented that preserved relationships across all systems.

2. Realistic Data

Your data masking technology solution must give you the ability to generate realistic but fictitious, business-specific test data. You want the data to work for testing but to provide  zero value to thieves and hackers. The resulting masked data values should be usable for non-production use cases. You can’t simply mask names into a random string of characters. 

For example, a financial services team once masked birthdates with random values, which unintentionally broke their fraud detection models. Switching to logic that maintained age distributions allowed them to preserve analytical value without compromising privacy.

3. Irreversibility

Irreversibility is essential, especially when data is shared outside trusted boundaries. The algorithms must be designed such that once data has been masked, you can’t identify the original values or reverse engineer the masked data. This is especially important when using data to tune AI models, as AI is exceptionally good at finding patterns in data. (Read more in this blog: The AI Compliance Crisis: Are You Prepared?)

4. Extensibility & flexibility

Extensibility and flexibility come into play when scaling across diverse systems. The number of sources involved in the average enterprise data pipeline continues to grow at an accelerated rate. To enable a broad ecosystem and secure data across sources, your data masking solution needs to work with the wide variety of data sources that businesses depend on.

For example, in a large banking environment, a policy-based approach allowed one masking rule to be applied across 20+ systems. This minimized manual effort and risk.

5. Repeatable & Automated

Repeatability and automation close the loop. A solution should not require re-engineering with every new dataset or business need. 

Masking is not a one-time process. Organizations perform data masking repeatedly as data changes over time. It needs to be fast and automatic while allowing integration with your workflows, such as SDLC or DevOps processes. 

Many data masking solutions add operational overhead and prolong test cycles. But with an automated approach, teams can easily identify and mask sensitive information such as names, email addresses, and payment information to provide an enterprise-wide view of risk and to pinpoint targets for masking. 

6. Policy-Based

With a policy-based approach, your data can be tokenized and reversed or irreversibly masked in accordance with internal standards and privacy regulations such as GDPR, CCPA, and HIPAA. Taken together, these capabilities allow businesses to define, manage, and apply security policies from a single point of control across large, complex data estates in real-time. 

What Are the Benefits of Data Masking? 

Your customers want to feel assured that their personal information is safe from bad actors. You also want to be able to safely use data to innovate, provide better experiences, and so on.

Done correctly, data masking protects the content of data while preserving business value. It helps maintain referential integrity across databases, supporting compliance with privacy regulations and reducing the risk of data breaches. With no need for programming expertise, organizations can confidently share data in non-production or cloud environments while minimizing exposure to threats.

Watch the video below to learn about the benefits of data masking for developers.