Top 10 Data Masking Tools: an In-Depth Look at the Current Market
Data masking tools are crucial when it comes to replacing sensitive data with realistic and structurally similar random values. They allow you to analyze reports effortlessly while complying with regulations. However, not all tools offer the same benefits. Curious about the key features of efficient data masking tools and how to solve the most common challenges when using them? In this article, we explore the current landscape of data masking solutions and present the 10 best tools to help you effectively protect your sensitive data.
Table of Contents
What is a data masking tool?
Data masking tools are sophisticated software solutions that effectively anonymize sensitive data. They render any Personally Identifiable Information (PII) contained in the raw data completely unrecognizable. As the primary layer of defense in data protection, these tools ensure that re-identifying data is impossible, which provides unparalleled security.
Must-have functionality of data masking tools
Before ranking the 10 best data masking tools, let’s set the criteria for choosing the best contenders. Which functions should a well-designed piece of software include to meet all compliance requirements? Let’s find out.
Security and compliance
The whole purpose behind database masking tools is to make your data secure and compliant.
To choose a superior tool, make sure it offers:
- Audit logging: It keeps detailed logs of masking activities for audit and compliance purposes.
- Regulatory compliance: Ensures compliance with data protection regulations through robust masking and protection mechanisms.
- Non-reversibility: Guarantees that masked data cannot be easily reverse-engineered to retrieve the original data.
Most popular tools have these features, but what really matters is how well they perform and how strong their security is. We’ll focus on that next.
Data discovery and classification
Data discovery involves identifying and locating sensitive data across the company’s systems and databases. The goal of this stage is to locate personal, confidential, or regulated information. In a perfect world, the system is capable of:
- Automated scanning of databases, files, and systems;
- Pattern matching to identify common data types (e.g., credit card numbers, SSNs);
- Content analysis to detect less structured sensitive information.
Once discovered, data is categorized by type and sensitivity to choose the right masking techniques. Depending on your needs, data can be classified by various criteria:
- Data type (e.g., financial, personal, health);
- Regulatory requirements;
- Business value;
- Potential impact if exposed.
The sensitivity levels are typically categorized as public, internal, classified, and highly classified.
Masking techniques
Different data masking techniques yield different outcomes and are suited to different use cases. What works well for non-product tests might be a terrible solution for developing products. Understanding how and where to use a particular masking technique is crucial.
Static data masking replaces sensitive data in a static dataset with fictitious yet realistic data before it’s used in non-production environments. This approach is called static because a copy is made and masked, and then the masked data is used.
Dynamic data masking anonymizes data on the fly as it’s accessed by different users. The actual data in the database is not changed.
Consistent masking ensures that identical original values are always masked to the same values to maintain referential integrity. This allows you to analyze and test data with realistic yet safe entries.
We covered different types of data masking techniques in more detail in one of our previous articles.
Modern database masking tools often support multiple methods, giving you flexibility across various scenarios.
Flexible masking algorithms
Flexible masking algorithms are a key feature of advanced data masking tools, providing adaptable and customizable methods for obscuring sensitive data. Data encryption, substitution, shuffling, date aging, generalization (binning), masking out, nulling (blanking), scrambling, hashing, and tokenization are the most common. We covered those data-masking algorithms extensively in one of our previous articles.
Most companies use these tried-and-tested algorithms, but other, more advanced methods powered by AI and ML quickly gain momentum. These tools are relatively new but have already shown their worth on the market, with Syntho at the forefront of AI-powered data-masking tools.
Data subsetting
Data subsetting involves creating a smaller, representative portion of a larger dataset. It can reduce data volume for non-production environments and improve the performance of testing and development processes. What’s more, it minimizes storage costs and processing time and helps you maintain data relationships and referential integrity.
When choosing a data subsetting tool, look for one that is efficient, flexible, and scalable, integrates well with other software, and has strong automation capabilities.
Support for multiple data sources
A top-notch data masking tool works with various types of databases, file formats, and data storage systems. Naturally, you’ll want to choose the one supporting your preferred formats, but in this review, we’ll give you a roundup of tools that operate with many different sources.
Scalability and performance
Performance-wise, the tool would ideally be able to handle terabytes or even petabytes of data and support distributed processing or parallel execution to manage high volumes effectively. You should also consider how quickly and efficiently the tool uses CPU, memory, and I/O resources.
For scalability, we check whether the tool is cloud-based and supports scaling, both horizontal (adding more machines) and vertical (upgrading existing hardware).
Usability and administration
The tool should have an intuitive, user-friendly interface accessible to non-tech-savvy users. Preferably, there should be customization options to tailor it to your liking.
The tool must also feature role-based access control for better oversight. Finally, the ability to configure automation is crucial, as it allows you to set the rules and let the tool run seamlessly in the background.
Regulatory requirements for the data masking tools
Companies implement data masking to comply with data privacy laws. These laws govern the use, storage, and sharing of sensitive data, dictating the security and privacy measures for handling it.
The regulated data includes PII and protected health information (PHI). PII refers to any data that identifies an individual, such as name, address, and SSN. PHI is a subset of PII and includes medical records, health insurance information, and any data related to an individual’s treatment.
Nearly all regulations are based on these key laws designed to protect PII and PHI:
- General Data Protection Regulation (GDPR) in Europe, UK-GDPR in the UK, Digital Charter Implementation Act (DCIA) in Canada, Act on the Protection of Personal Information (APPI) in Japan, Personal Data Protection (PDP) Law in Indonesia, and other similar acts.
- Health Insurance Portability and Accountability Act (HIPAA) for healthcare organizations in the US.
- Payment Card Industry Data Security Standard (PCI DSS) for businesses that handle credit card information.
- California Consumer Privacy Act (CCPA) and California Privacy Rights Act (CPRA).
- The Sarbanes-Oxley Act (SOX) for financial record keeping and reporting for corporations in the US.
Masking techniques help organizations comply with these regulations by eliminating all direct and indirect identifiers. After masking, the datasets become de-identified or anonymized and thus excluded from data privacy laws.
Challenges of data masking tools
All data masking tools face similar challenges. To choose the best tool for your company, you need to understand these issues and find a product that addresses them most effectively.
Sensitive data search and identification
Organizations usually work with large amounts of data spanning various systems, databases, and file formats. This data is often non-uniform or unstructured, which only complicates the process.
Moreover, information might be context-sensitive, changing its meaning when viewed in isolation. Data can change over time, and what is considered “sensitive” may shift, too.
Identifying all instances of data in this environment is costly and time-consuming. However, a robust tool can streamline this complex process, cutting costs.
Maintaining data integrity
Data masking solutions must maintain the integrity of data across tables and databases, or the data will be unusable. The main elements of data integrity include:
- Format preservation: Your metadata must be accurately classified to preserve the formats of ID numbers, telephone numbers, email addresses, etc.
- Referential integrity: Maintaining referential integrity ensures the smooth operation of enterprise systems in lower environments where sensitive data is masked.
- Semantic integrity: Data has to be masked in such a way that its meaning is preserved in the particular context.
- Gender preservation: If names are changed randomly, the gender distribution in a table will change, too.
- Data uniqueness: If sensitive data in a dataset is unique, such as an SSN, the data masking tool should apply unique values to each data element. This technique should have built-in collision-avoidance functionality.
Scalability
Organizations that use data masking face challenges due to the sheer scale of their operations. The daily business flow generates new records and builds massive data sets. Therefore, data masking solutions must be capable of handling large databases to avoid putting a massive strain on infrastructure.
Performance
Data masking is often resource-intensive and impacts system performance. This requires additional hardware or software investments, especially for more complex techniques. A static data masking tool will consume some bandwidth, while dynamic data masking tools typically use more resources and reduce performance unless radically optimized.
Repetition
Applying the same masking rules to unchanged data over and over wastes computational resources. Good caching can solve the problem, but it’s not straightforward. You need to carefully balance keeping the cache up-to-date with achieving the best performance.
Top 10 data masking tools
Finally, we’re getting to the core of our article. Let’s take a look at the top 10 data masking tools on the market and see how they compare.
1. Syntho
Syntho data masking approach eliminates the need for manual data masking, which is inefficient and prone to human errors. Manual processes take too much time and might result in incomplete masking or useless data. In contrast, smart masking technology ensures consistent PII protection and compliance.
Syntho offers automated data masking solutions to protect sensitive information across all data sources with the following capabilities:
- De-identification and synthetization: Generates test data that reflects production data for comprehensive testing and development in representative scenarios.
- Rule-based synthetic data: Produces synthetic data to mimic real-world or targeted scenarios using predefined rules and constraints.
- Subsetting: Reduces records to create a smaller, representative subset of a relational database while maintaining referential integrity.
Try our demo to see how our tool can help you achieve compliance without compromising quality.
2. Informatica
Informatica supports data discovery, creation, and subsetting. The product scales both horizontally and vertically and integrates with DevOps CI/CD pipelines.
This platform provides data masking for raw production data to ensure compliance. Comprehensive documentation and responsive technical support ease the onboarding process.
Downsides include a complicated initial setup and the data subsetting process, which can be painfully slow with larger datasets. Though not a major issue, the interface feels somewhat clunky and outdated.
3. K2view
4. DATPROF
DATPROF offers a robust data masking solution as part of its test data management suite.
DATPROF boasts data consistency across multiple apps and databases and performs well on high-volume databases. The solution features an intuitive interface and pre-built masking rules that can speed up implementation.
However, being relatively new, the tool lacks many critical features. The users are particularly vocal about the absence of certain file formats and third-party integrations.
5. Delphix
Delphix automates data masking across various sources, handling large volumes efficiently via distributed processing.
It simplifies policy creation with an easy interface and predefined rules, while identifying sensitive data across structured and unstructured sources. Delphix ensures data consistency and referential integrity in different environments and offers reporting.
However, its steep learning curve and high cost can push away some users, affecting the tool’s return on investment.
6. BizDataX
BizDataX is a data masking solution known for its holistic approach to anonymizing data. It effectively disguises sensitive data while preserving its original structure and usability.
The tool offers an end-to-end workflow covering discovery, masking, and provisioning, ideal for companies seeking an all-in-one solution. Another key feature is a Test Data Management (TDM) portal to effectively manage test data across the enterprise.
Some users, however, report issues with importing data models and implementing custom GUI.
7. Immuta
Immuta excels in dynamic data protection. It implements masking policies dynamically without duplicating or relocating data while maintaining its integrity and reducing storage overhead.
The use of proprietary Privacy-Enhancing Technologies (PETs) strengthens data security.
Potential concerns include vendor lock-in and challenges of integration with existing systems due to the reliance on proprietary technologies. Users also complain about infrequent updates and issues with customer support.
8. IBM InfoSphere Optim Data Privacy
IBM InfoSphere Optim Data Privacy handles extensive data masking operations well, making it ideal for complex, high-volume data environments.
Integrating seamlessly with other IBM products, it’s a budget-savvy option for companies that have already invested in IBM technologies.
That said, users struggle with the tool’s clunky UI, feature overload, and a steep learning curve.
9. Skyflow
Skyflow stands out as a data privacy vault tailored for regulatory compliance, in particular, GDPR.
Skyflow’s core strengths are zero-trust data security principles and privacy by design, which create a robust framework for protecting sensitive data. Its focus on compliance will appeal to organizations grappling with stringent data protection laws.
However, it needs to simplify its pricing system and streamline user experience to gain traction.
10. Broadcom Test Data Manager
Broadcom Test Data Manager, formerly CA Test Data Manager, addresses the challenge of managing test data in Continuous Delivery environments.
It combines data subsetting, masking, and synthetic data generation, supporting agile software development needs.
One of the downsides is the need to heavily invest in training and setup to fully leverage the tool’s potential.
Conclusion
While all tools on this list are great, Syntho stands out as the most comprehensive solution. It integrates all key modern data generation methods, providing exceptional flexibility to tackle diverse tasks with an all-in-one tool.
Syntho’s most compelling feature is its AI-driven approach to synthetic data generation. Our models learn your data and its properties and effectively synthesize new data to mask sensitive information.
We’re also proud of our transparent pricing system with a fixed license fee that grants access to all features without additional usage charges.
Syntho offers automated data masking solutions to protect sensitive information across all data sources. Try our demo to discover how it ensures compliance without compromising data quality.
About the author
Business Development Manager
Uliana Matyashovksa is a Business Development Executive at Syntho, with international experience in software development and the SaaS industry, and holds a master’s degree in Digital Business and Innovation, from VU Amsterdam. Over the past five years, Uliana has demonstrated a steadfast commitment to exploring AI capabilities and providing strategic business consultancy for AI project implementation.
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