Table of Contents

    Table of Contents

    Open-Source Data Masking Tools: Can You Afford to Go Cheap?

    Amitai Richman

    Amitai Richman

    Product Marketing Director

    Study the benefits and risks of open-source data masking tools vs commercial solutions for both static and dynamic data masking. 

    What are Data Masking Tools and Who Needs Them? 

    Data masking tools are software solutions that nullify, encrypt, or replace sensitive information to ensure privacy compliance while keeping the data usable. Common data masking techniques include date/number variance, nullifying, scrambling, shuffling, and substitution.

    If your business collects, stores, or uses sensitive data or any Personally Identifiable Information (PII), it needs data masking tools.

    As with any software solutions, organizations looking to adopt data masking tools have a choice.  

    For budget-conscious companies, there are open-source data masking tools such as:

    However, enterprises require a higher level of data masking functionality and features, alongside reliability. That’s because the regulatory stakes are quite high – with non-compliant companies forced to pay millions of dollars in penalties.   

    This article will explain why the choice between open-source and commercial data masking tools is really a question of, “Can we afford to go cheap?” 

    Get Gartner’s guide to data masking for FREE

    Benefits of Open-Source Data Masking Tools  

    Open-source data masking tools have 1 major advantage over commercial solutions: They’re free (or at least low-cost).  

    Beyond this obvious benefit, open-source tools are considered more flexible and customizable – because programmers can access and modify the source code to build their own tools.  

    What’s more, open-source data masking tools are seen as transparent, since experienced developers (if available) can review and verify the application code or algorithms and report issues.

    That said, open-source data masking comes with some notable risks, as we’ll see below. 

    Risks of Open-Source Data Masking Tools 

    While open-source data masking tools may be suitable for smaller-scale projects that don’t fall under regulatory oversight – enterprises should think twice. Why? Because open-source data masking tools are NOT necessarily: 

    1. Scalable 

      Open-source tools frequently can’t handle the massive volumes and performance demands of enterprise data networks. Such scalability is crucial to efficiently mask data at the enterprise level and avoid performance issues or overly lengthy processing times. 

    2. Secure 

      Open-source data masking tools may not meet the strict security and Role-Based Access Control (RBAC) standards dictated by enterprises. And they may not have undergone the rigorous testing that commercial solutions undergo. Also, they may lack the regular updates, security features, and dedicated support teams that can address emerging cyberthreats. 

    3. Reliable 

      Because they’re free or low-budget, and generally not supported by dedicated development and QA teams, open-source data masking tools may not have been enterprise-qualified for product quality, performance, and reliability. As a result, should the software fail to obfuscate data in test environments, an internal or external data breach could have dire consequences. 

    4. Compliant 

      Compliance with data privacy laws (like GDPR, CPRA, and HIPAA) is important for any organization but for enterprises it’s critical. Open-source data masking tools may lack key compliance features, reporting and support, making it challenging to ensure that the data masking techniques used align with specific regulatory requirements. 

    5. Enterprise-grade 

      Open-source data masking tools often lack enterprise-grade functionality like PII discovery, unstructured data masking (e.g., of images and PDFs), referential integrity enforcement, advanced masking functions (beyond simple text replacements), and connectivity with any data source, including NoSQL databases. 

    6. Well supported and maintained 

      Open-source data masking tools may fall short on ongoing support and maintenance, which is often dependent on the goodwill and availability of volunteer developers. Lacking dedicated and professional support teams, it may be difficult to resolve issues or receive assistance within a reasonable timeframe. And software delivery can be delayed by data masking bottlenecks during provisioning. 

    7. Interoperable 

      Open-source data masking tools frequently fall short on interoperability, in the sense that they may not integrate smoothly with existing systems or workflows – impacting not only security but also productivity. 

    8. Feature-rich 

      Free or discount tools generally offer basic features only. Enterprises, on the other hand, require toolsets that can handle high-volume, high-performance workloads – and support multiple data types, formats, and technologies. 

    Enterprise-Grade Data Masking Tools from K2view 

    Enterprise-grade data masking tools, like those from K2view, discover and classify PII/PHI automatically. Patented entity-based data masking technology scans metadata and database content, and then applies data masking best practices for full regulatory compliance.

    The K2view solution ingests data from any source data, organizes it by business entity (customers, devices, or orders), to perform static and dynamic data masking in-flight with full referential integrity for operational and analytical use cases.

    Learn more about K2view entity-based data masking tools

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