Data masking replaces PII with fake but realistic data. Learn about the top tools for 2025: K2view, EPI-USE LABS, Oracle, IBM, Camouflage, and Informatica.
What are Data Masking Tools?
Data masking tools are software products that anonymize sensitive data, rendering any Personally Identifiable Information (PII) contained in the raw data indetectable. They’re generally considered to be the first line of defense in data protection because, if done right, re-identifying the data would be impossible.
Data masking tools help companies secure data in various industries, such as:
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Financial Services
PCI-DSS, the Payment Card Industry Data Security Standard, is an information security standard created to control cardholder data and reduce credit card fraud.
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Healthcare Services
PHI, Protected Health Information is any health status data, including the provision of, and payment for, healthcare services – that can be linked to a specific individual.
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Commerical Services
PII, Personally Identifiable Information, is any data that can be used to identify someone, including names, addresses, telephone numbers, and Social Security Numbers.
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The Need to Comply with Global Data Privacy Laws
Choosing the right data masking tool ensures you stay compliant with the growing range of international data protection regulations, including:
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GDPR and DORA
The EU’s General Data Protection Regulation of 2016 and Digital Operational Resilience Act (DORA) of 2024 spell out the obligations that companies must perform to endow individuals with control and rights over their personal information. DORA compliance is more focused on personal financial data stored by EU firms.
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CPRA
California’s Privacy Rights Act of 2020 extends its Consumer Privacy Act of 2018 specifying data protection obligations for businesses to carry out to provide privacy rights to consumers. Although spearheaded in California, many other US states have since followed suit.
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HIPAA
The US Health Insurance Portability and Accountability Act of 1996 requires healthcare providers to protect sensitive patient health information from being disclosed without the patient’s consent or knowledge.
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SOX
The Sarbanes-Oxley Act of 2002 is a US federal law that requires transparency and accountability in financial record keeping and reporting for corporations.
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DCIA
Canada’s Digital Charter Implementation Act of 2020 provides individuals with control and rights over their personal information as first dictated by GDPR, and further focused by DORA European regulations.
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APPI
Japan’s Act on the Protection of Personal Information of 2003 regulates the handling of personal information by individuals, government agencies, businesses, and non-profit organizations.
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PDP
Indonesia’s Personal Data Protection Law of 2022 regulates the collection, use, disclosure of personal data by international organizations and governmental and private entities.
What to Look for in a Data Masking Tool
Consider investing in a future-ready data masking tool with full functionality, otherwise you may need to add additional point solutions to fill in the gaps. Make sure the data masking software you choose offers the following features and capabilities:
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Integration with any data source
Many data masking tools are only able to extract data from specific sources. For an enterprise with many different databases, it’s critical that the solution you choose can easily access – and automatically update – data from ALL sources, from legacy mainframe systems and SAP, to MongoDB and NoSQL databases.
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PII Discovery
Data comes in many different formats, which is why it’s important the tool you choose can automatically detect PII scattered all over the enterprise, and can map schema relationships.
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Dynamic data masking
An enterprise-grade data masking tool must be able to dynamically determine who can have access to real data, and to what extent. Dynamic data masking decreases the risk of internal security threats while still enabling individuals and teams to access data when they need it.
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Wide choice of data masking techniques
It’s critical to employ a software that has the ability to work with multiple data masking techniques, like anonymization, pseudonymization, redaction, shuffling, and more. This gives your platform team the flexibility to choose the one that best suits each use case.
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Ability to mask unstructured data
Unstructured data often contains PII, so unstructured data masking is imperative. This can be anything from images and PDFs to XML files and chats. It’s often difficult to detect PII when it’s in unstructured data, but it’s no less important since regulatory bodies do not differentiate between structured and unstructured. Your data masking tool must be able to detect and mask sensitive information in both structured and unstructured formats.
Comparison of the Top Data Masking Tools
Below is a comparison of 6 of the most popular data masking tools, listing the pros and cons for each:
1. K2view – Data Masking for Complex Environments
K2view’s data masking capabilities are built into its data product platform, offering fast, scalable masking across structured and unstructured data – all while preserving referential integrity. Ideal for enterprises with complex data architectures.
| Criteria | Details |
|---|---|
| Best For | Enterprises with complex, multi-source data needing scalable, fast masking |
| Key Features | Entity-based masking, in-flight masking, referential integrity, unstructured support |
| Pros | Fast, scalable, masks in motion, preserves data relationships |
| Cons | Less known vendor, newer to market |
| User Rating | 4.5 / 5 |
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2. EPI-USE Labs – SAP-Centric Data Masking
EPI-USE LABS specializes in masking data within SAP environments, making it a go-to solution for companies heavily reliant on SAP systems.
| Criteria | Details |
|---|---|
| Best For | Organizations focused on SAP data protection |
| Key Features | SAP integration, simple deployment, masking transparency |
| Pros | Easy to use, tailored for SAP |
| Cons | Limited to SAP, unclear UI, needs separate tools for non-SAP data |
| User Rating | 4.0 / 5 |
3. Oracle – Data Masking and Subsetting
Oracle Data Masking and Subsetting tool offers native data masking within its ecosystem, enabling secure subsetting and masking for Oracle databases.
| Criteria | Details |
|---|---|
| Best For | Companies using Oracle databases exclusively |
| Key Features | Tight Oracle DB integration, subsetting, automation |
| Pros | Strong Oracle integration, simplifies Oracle data protection |
| Cons | Complex for non-Oracle use, limited to Oracle stack |
| User Rating | 3.8 / 5 |
4. IBM InfoSphere Optim – Enterprise-Grade Masking
IBM InfoSphere Optim Data Privacy tool offers enterprise-wide data masking with subsetting and privacy tools, though its user experience is often criticized.
| Criteria | Details |
|---|---|
| Best For | Large enterprises with diverse and legacy systems |
| Key Features | Data subsetting, privacy controls, wide integration |
| Pros | Scalable for large orgs, handles complex data environments |
| Cons | Outdated UI, limited integrations, steep learning curve |
| User Rating | 3.6 / 5 |
5. Camouflage – Straightforward Data Masking
Camouflage Software focuses on ease of use and rapid implementation, with support for various formats and databases.
| Criteria | Details |
|---|---|
| Best For | Mid-size teams needing easy-to-implement masking |
| Key Features | UI simplicity, file format support, flexible deployment |
| Pros | User-friendly, quick setup |
| Cons | Feature limitations for complex use cases |
| User Rating | 3.9 / 5 |
6. Informatica – Cloud-Based Data Masking
Informatica Cloud Data Masking delivers versatile data masking across cloud and hybrid environments, with strong integration into its broader suite.
| Criteria | Details |
|---|---|
| Best For | Enterprises needing cross-platform, cloud-compatible masking |
| Key Features | Persistent masking, broad compatibility, cloud readiness |
| Pros | Versatile, integrates well with Informatica tools |
| Cons | Expensive, steep learning curve, limited support |
| User Rating | 4.1 / 5 |
Choosing Enterprise-Grade Data Masking Technology
In the data masking tools comparison, you need to consider how the solution will scale with your business. According to a recent Gartner report, data masking is becoming increasingly challenging as data environments become more complex. And there’s a definitive need to discover PII automatically across various systems and mask data in any data source, including modern NoSQL databases.
Looking ahead, as a company’s data management needs grow, choosing the right data masking technology now, will certainly pay off down the road.
