The 5 best data masking software solutions for 2026

Most businesses understand the need to protect customer data, but many have a more lax attitude toward using it in non-production environments. Data being out of production cycles doesn’t necessarily mean it’s safe, and breaches can still happen wherever security controls are loose.

Data masking software shields sensitive data by replacing it with realistic (but false) information that you can use safely in non-production workflows like testing and development.

In this guide, we explore the best data masking tools on the market to help you choose the right platform for your business. 

What are data masking tools?

Companies may want to use realistic data for testing or product development, but working with real customer information is a security risk. Data masking tools are software solutions that scan your documents for sensitive data and replace it with falsified information. Changes include replacing numbers or letters and censoring or removing parts of a file. The goal is to prevent people from being able to reverse-engineer the original content.

Obfuscating sensitive data makes it safe for non-production environments. For example, a developer or tester can use altered data without running into compliance issues or exposing sensitive information to a potential breach.

Why is data masking important?

Data masking solutions are an integral part of your compliance strategy. Changing and obscuring sensitive data works as an additional layer of security, helping you meet your legal and regulatory obligations. Masking tools are enormously useful in keeping customer data safe, especially in compliance-heavy industries like finance and healthcare.

By using masked information, your developers can check for software bugs without exposing actual customer data. Test data masking lets you use content in non-production environments as much as you want while remaining compliant. 

Top 5 data masking tools

There are many ways to go about data masking, and each software platform has its own approach. To help you pick the best solution for your purposes, we’ve put together a list of the most popular tools and explained what sets each one apart.

1. Delphix: Best for AI-ready data masking

Perforce Delphix for Data Masking identifies sensitive information across data warehouses, pipelines, and databases automatically, using AI to mask any values that you’d prefer to keep private. The replacement values are realistic, allowing you to use generated data for non-production workflows.

The platform is highly scalable, helping you to construct policies and deploy them across even enterprise environments, and its fictionalized data is especially useful for training AI models. 

Delphix is a great choice if you need enterprise security and an out-of-the-box solution for advanced masking scenarios.

2. Informatica data masking: Best for building a complete data ecosystem

While Informatica doesn’t offer a product solely focused on data masking, you can use the platform’s governance offering for dynamic data masking (DDM) and persistent or static data masking (SDM). It allows you to establish predefined rules for modifying data and apply them continuously.

You can use the governance system by itself, but it works best when paired with Informatica’s other solutions, like its data cataloging and integration services. This makes Informatica a good option for companies that need to build their data architecture from scratch.

3. IBM InfoSphere Optim: Best for test data management at scale

IBM InfoSphere Optim is an all-in-one data management system that includes comprehensive data masking capabilities. It’s designed to be a central part of your broader privacy and lifecycle strategy, helping you better understand how data works in your system and what you can do to protect it. As well as data masking, Optim supports archival and retirement, so you can enforce compliance policies across your entire environment. 

Optim can be a useful option for teams that need to mask information in support of a wide range of use cases, like delivering test data to other systems at scale. Its enhanced monitoring capabilities, which span from source to storage, allow you to meet even complex compliance requirements while keeping your data secure. 

4. Oracle Data Masking and Subsetting: Best for Oracle-centric workflows

Oracle Data Masking and Subsetting is built for organizations that need non-production data but want to avoid major storage costs. The platform’s data replacement feature allows you to create smaller, targeted subsets of information for testing and development purposes. 

Masking and Subsetting connects to Oracle Database, making it easier to generate test data if you already use the Oracle product suite. It allows you to configure exactly what you want to mask, only extracting slices of data as required. This reduces storage costs by eliminating the need to duplicate entire datasets.

5. Microsoft SQL Server Data Masking: Best for Microsoft-native data security

Microsoft SQL Server Data Masking aims to limit the accidental exposure of sensitive data. Instead of completely transforming information, the platform masks fields when a user tries to query them, obfuscating data instantly if they lack the relevant permissions.

This means that to apply Microsoft SQL Server Data Masking, you’ll already need to have an extensive network of permissions. Because of this, the platform works best as a complementary security control, rather than a stand-alone system.

If you need a quick way to limit data risks and you already use SQL Server, the platform’s data masking offering can be a great option.

How to choose the right data masking tool?

Finding the right data masking tool depends on several variables, including the size of your organization, the type of data you want to work with, your industry, and any additional compliance requirements you’re subject to. 

For example, smaller businesses might prefer a tool that’s easy to set up and doesn’t require much technical knowledge to use. Larger enterprises will probably have teams with a higher level of technical expertise, so they might wish to choose a platform that uses more complex data masking techniques. 

Another major consideration is whether a masking tool will fit into your existing data ecosystem. Any platform you pick will have to connect to your pipelines, storage, and analytics engines. Be sure to inspect your tech stack to check whether your chosen software works with your existing architecture.

Secure your data pipeline with Fivetran

Data masking is an effective way of protecting ingested data, but it’s just one part of a wider security strategy. Fivetran complements your data security tools, providing robust ingestion pathways that get high-quality data from A to B while meeting compliance standards. 

Fivetran Security offers end-to-end encryption, SSH tunneling, and column blocking to ensure your sensitive data remains private. From the moment source content enters Fivetran’s fully automated ELT pipelines to the moment it lands in your warehouse for masking or analysis, your data remains secure.

To see how Fivetran can support your business as it scales its data infrastructure, book a live demo today.

FAQs

Can you provide an example of data masking?

One way data masking could protect information is by identifying and replacing customers’ social security numbers so they can be used for product testing. By randomizing each number, data masking software can create duplicate fields that serve the same purpose but don’t put customer information at risk.

What is the best data protection software?

The best data masking companies and software solutions will depend on your business, sector, and the type of data you’re looking to mask. For example, larger organizations might prefer IBM InfoSphere Optim’s all-in-one data management solution, while companies that already use the Oracle product suite would probably get the most out of Oracle Data Masking.

How to make data unreadable?

To make data unreadable, you can use strategies like number and letter replacement, randomly substituting data in these fields to ensure the content doesn’t represent real customer information.

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