Effective Tips for Choosing the Right Data Masking Techniques

Data is one of the most precious assets for an organization. As more companies rely on data for decision-making, planning, and management, the significance of data security grows rapidly. Security threats like data breaches, unauthorized access, and data leaks result in severe financial, legal, and reputational loss. It was also mentioned that one of the most effective measures of data protection is data masking. 

Data masking guarantees the conversion of sensitive information into an appropriate form of usage within testing and development while making it secure. This article will provide guidelines on how to select the most appropriate data masking solutions that will guarantee adequate protection of data while not limiting functionality.

Understanding Data Masking

Data masking is the technique of modifying data such that the actual values are concealed and substituted with other values that are safe to be shared. Various tried and tested data masking techniques make sure that information employed in non-production endpoints remains relevant for that context but is not actual or confidential. 

However, it is important to note that masks should not alter the structure, format, or characteristics of the original data in a way that interferes with the functioning of applications or processes that employ the data.

Methods of Implementing Data Masking

Before diving into the tips for choosing the right data masking techniques, it is essential to understand the various types of data masking available:

Static Data Masking (SDM): This technique involves obscuring data at rest, usually at structures such as databases. However, once the data is masked it becomes permanently concealed and cannot be restored to its original form.

Dynamic Data Masking (DDM): This works by masking data as soon as it is retrieved by an application or a user in real time. The actual values of the data are still stored in the database while only the masked values are computed and based on the user roles and permissions.

Deterministic Data Masking: This makes sure that when a particular input has been given, the mask that is generated is always the same. This is beneficial in keeping similar information uniform across various databases or applications.

Non-Deterministic Data Masking: This technique gives different masked values for the same input and hence is more secure than the previous one.

Format-Preserving Masking: This technique retains the original data layout; For example, date formats, structures of credit cards, and social security numbers, while changing the data values.

Shuffling: This method involves sorting the values in ascending or descending order throughout the column while the value set remains the same.

Nulling Out: Such fields are simply assigned to null or predetermined constant values which render them useless but secure.

Strategies for Appropriate Data Masking Procedures

Several strategies can be used to choose and apply data masking procedures in a company. A data manager should know all the techniques and reasons why data masking is done and how it will benefit the company. Below are examples of strategies applied in data masking:

Assess Data Sensitivity

Start by evaluating the level of risk your data contains. Determine which of the data elements are considered high-risk and should be masked. Examples of such information include personally identifiable information (PII), financial data, health records, and proprietary business information. 

Knowing the sensitivity of the information will also assist in identifying the proper masking method and the extent of the necessary masking.

Consider Compliance Requirements

Specific fields have unique compliance requirements including GDPR, HIPAA, PCI DSS, and CCPA. Such regulations tend to prescribe how personal information should be processed, often including provisions for data masking. 

Make sure that the chosen data masking technique complies with the legal requirements of the respective compliance standards to escape the legal consequences and safeguard data.

Evaluate Data Masking Goals

Decide very clearly what goals you want to solve with the help of data masking in your organization. Would you like to protect development and test environments, BI and analytics, or meet compliance and security needs?

This will help you to identify the most appropriate data masking strategy in the context of the identified goals. For example, if the goal is to protect data in the testing and development context, so static data masking can be used. If the need is to guarantee real-time protection during data access then it may be best to go for dynamic data masking.

Determine Data Usage Patterns

Get insight into how data is processed in the context of your organization. Identify which programs, operations, and users are dealing with such data. Understanding how data is utilized will help in the determination of a masking methodology that may complement other methods. For instance, if many applications require the same masked data, a deterministic approach ensures that the images are identical.

Assess Performance Impact

With data masking, this should not cause considerable effects on system performance. Assess the impact of different masking types on performance, focusing on dynamic data masking since it works in real-time. 

Validate the selected masking approach to check whether it has the desired performance while passing testing without compromising the real data processing speed.

Ensure Data Consistency

More so for organizations that have different databases or systems in place, data consistency is very important. Deterministic data masking enables data to have similar masks regardless of the system in which they are used. This is particularly relevant for applications that employ data mapping or connection of multiple databases.

Prioritize Ease of Implementation

Look at the complexity of applying the data masking technique in your current environment and architecture. Depending on particular techniques some alterations might affect databases or applications dramatically, while others may be incorporated without much difficulty. Select a technique that minimally disrupts your processes and is feasible with your technological resources.

Maintain Data Usability

Data masking on the other hand tries to hide the sensitive data but it should not render the data absolutely useless. The chosen masking technique should retain the usability of the data to further test, develop, or for analysis purposes. For instance, format-preserving masking ensures that data remains usable and comprehensible even after masking.

Leverage Automation Tools

Another added feature in the current data masking tools that users get involves automation of the process of masking. Therefore, it is advisable to use these tools to achieve consistent and effective data masking. Masking can also be applied uniformly as opposed to human input which could lead to error in most cases.

Plan for Future Scalability

Think about how your chosen data masking solution can be used for future growth and scalability. Another aspect your chosen technique must address is scalability and how it will perform as your organization expands and collects more data. Ensure that any planned and implemented technique can grow over time without needing frequent reconsideration.


What is the difference between Static Data Masking and Dynamic Data Masking?

Static Data Masking (SDM) is the process of masking data which is in a static state meaning it has been modified and cannot be changed back. But, Dynamic Data Masking (DDM), hides data on the fly when it is used in an application, or by a user. The real data remains intact and stored in the database, while only the masked values are displayed depending on the user role and permissions.

What role does data masking play in achieving compliance?

Data masking plays a vital role when it comes to adhering to different regulations like the GDPR, HIPAA, PCI DSS, and CCPA by shielding data from unlawful access. By hiding sensitive information, an organization can be confident that it is in compliance with organized norms of data privacy and security, avoid further legal consequences, and prevent data breaches.

Does data masking have the potential to affect system performance?

Yes, data masking does affect the performance of the system and this is particularly the case with dynamic data masking that occurs in real time. There is a need to determine the performance consequences of various masking strategies and to experiment with the chosen approach in order to check its performance characteristics and to see whether it compromises performance and creates latency or throughput problems.


Selecting the proper data masking method is essential for preserving delicate data while making the data suitable for various purposes, including testing, development, and analysis of data. Through data sensitivity, analyzing compliance regulation, measuring the masking objectives, analyzing data utilization, and data coherence, an organization can choose the adequate data masking technique. 

Moving forward, giving higher priority to low implementation costs, employing automation tools, and anticipating future increases in scale will help to strengthen data protection activities. That is why organizations need to have the right approach to data masking and protection of their valuable data assets.

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