Data Masking Solutions: Choosing the Right One

With so many data masking solutions out there, it's hard to know which is best for you. Understanding the use cases, risks, and features helps you decide.

Table of Contents

Data masking solutions are good for business 
Making the case for data masking solutions
Risks of NOT having a data masking solution
Data masking solution requirements 
Top data masking solutions 
Why entity-based data masking solutions are no. 1

Data masking solutions are good for business

Data masking (aka data anonymization) protects sensitive and confidential data by obscuring it or replacing it with fictional yet realistic data. DevOps, security, and compliance teams agree that a data masking solution is an essential component of data governance tools. Specifically, it facilitates compliance with privacy regulations, prevents data breaches, and keeps sensitive data secure during software development and testing. 

Data masking tools can be used to protect a wide variety of sensitive data, such as PII (Personally Identifiable Information), financial information such as credit card details, and Protected Health Information (PHI). However, having the right data masking solution is fundamental to ensuring data security and compliance.

In this article, we’ll cover common use cases, the risks of not having an effective data masking solution, how to choose the right solution, and leading vendors on the market today.

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Making the case for data masking solutions

Here are 4 ways enterprises put their data masking software to work:

1. Protecting data during software development and testing
   Software development and testing teams require realistic, complete, clean, and reliable data. Using real production data, which often contains sensitive information, increases risk of a data breach and noncompliance with data privacy and protection laws. Instead, a data masking solution ensures development and testing data is adequately obfuscated while maintaining its functionality.

2. Compliance with data privacy regulations
   Complying with data privacy laws, such as GDPR, CPRA, HIPAA, and PCI DSS, can be extremely complex, especially if you try to implement rules and monitor compliance manually. A dynamic data masking solution makes compliance significantly easier, by automating the manual coding and scripting of every database, each with its own separate formats, technologies, and terminologies.

3. Data security
   The act of masking data ensures that sensitive data remains protected and confidential in the event of a breach. This is crucial for enterprises today, as the rate of internal, external, and third-party breaches continues to rise. Anonymized data is useless to a hacker. Even though it looks real, it can’t be used to identify a real person or event, or to make a fraudulent transaction. Masking your sensitive data minimizes security risks.

4. Customer 360
   A data masking solution allows business users throughout the organization to safely access a customer 360 platformwithout exposing any personal or sensitive information. Business users gain a single customer view (including interaction, transaction, and master data) while remaining compliant with data privacy laws and security policies.

Risks of NOT having a data masking solution 

Organizations that don’t implement a data masking solution face a broad range of serious risks, including:

1. Data breaches
   Without proper data masking techniques, sensitive information is vulnerable to theft, hacking, and other security breaches that could result in the loss or exposure of confidential information.

2. Compliance violations
   Not having the right data anonymization tools makes it difficult to comply with data privacy laws. Failure to comply with GDPR, CRPA, and other laws comes with costly consequences: noncompliance can lead to stiff penalties (4% of a company’s annual turnover, or €20 million – whichever is lowest). On top of that, litigation expenses can drive up costs for years.

3. Reputational damage
   Data breaches and privacy violations can significantly damage your organization's reputation and brand image, leading to a loss of trust and customer loyalty. Without an effective solution for keeping data secure such as data masking, your brand reputation – and ultimately, your ability to attract and retain customers – is at risk.

4. Insufficient testing
   Without a data masking solution, it can be difficult for software development and testing teams to gather or provision enough test data. Without adequate test data, the development lifecycle will slow down, and the software itself is more likely to have issues.

5. Lack of control over sensitive data
   Organizations that do not use a data masking solution have limited control over sensitive data, especially when it comes to third-party access. Data masking solutions give organizations the ability to control and monitor who has access to sensitive data. 

Data masking solution requirements 

With consideration for your organization’s unique needs and goals, the data masking solutions you choose should:

1. Enable a variety of masking methods
   The right solution will allow you to use a broad range of data masking methods or techniques to support different use cases. Some of the most useful methods include data anonymization, pseudonymization (e.g., tokenization), encrypted lookup substitution, redaction, shuffling, data aging, nulling out, and more.

2. Be scalable
   Data masking solutions should be able to handle large volumes of data and accommodate growth over time. You’ll want a solution that can scale up as your data requirements increase.

3. Easily integrate with your existing systems
   Choose a solution that integrates easily with your existing and future data stores and workflows. Many organizations have separate, standalone masking tools for each database, or database vendor. Having one tool that can mask data from all data sources saves on licensing fees and simplifies managerial, operational, and legal complexities.

4. Fit your budget
   In addition to the cost of the tool itself, make sure to check other fees, such as licensing fees, support, and maintenance costs, to ensure the total cost fits your organization’s budget.

5. Ensure relational consistency
   The data masking solution you select should represent masked data consistently throughout all your systems. This requires masking every type of data originating from a certain business system with the same algorithm (“referential integrity"). The most effective data masking solutions will have the ability to automatically apply the same types of data masking techniques and algorithms to the PII in various data sources.

6. Provide reporting and auditing functionality
   Choose a data masking solution that has built-in reporting and auditing capabilities, suitable for both external and internal audits. This will help simplify your efforts to enforce compliance with data privacy laws and company security policies

Top data masking solutions 

To help narrow down your search, here’s our shortlist of today’s top data masking solutions. For a deep dive into each solution, check out the article Top 5 Data Masking Vendors for 2023.

1. K2view
   K2view data masking capabilities include dynamic and static masking, in-flight transformations and masking, as well as structured and unstructured data masking.

2. Broadcom
   The Broadcom Test Data Manager combines elements of on-demand data generation, masking, and subsetting.

3. IBM
   The IBM InfoSphere Optim Data Privacy solution replaces sensitive data with realistic, contextually accurate, fictional data.

4. Informatica
   The Informatica Persistent Data Masking solution secures sensitive data via data anonymization and encryption for use by analytics, development, and testing teams.

5. Datprof
   The Datprof Privacy solution is designed to mask data consistently across multiple tables, systems, or cloud applications.

Why entity-based data masking solutions are no. 1 

Entity-based data masking technology offers the most robust data masking solution today. It allows for a broad range of data masking techniques, scales according to your needs, always assures referential integrity and semantic consistency, simplifies compliance, and easily integrates into your existing tech stack.

Unlike most alternatives, entity-based data masking delivers all the data related to a specific business entity (customer, order, device, etc.) to authorized data consumers, masking data on the fly.

By taking an business entity approach, enterprises improve test data management, accelerate development cycles, achieve customer 360, and gain greater control over data governance. It protects data at rest, in use, and in transit, to maximize data security and functionality.

Learn more about K2view data masking tools, the ultimate data masking solution.