What is Data Masking? Types and Techniques

Data masking is a data security strategy that involves copying a dataset while concealing the sensitive information. Then, for testing or training reasons, this safe duplicate is applied in place of the original data.

 

Data masking does more than merely substitute blanks for private information. In order to maintain the intricacy and unique properties of data, it makes copies of personally identifiable information or other highly sensitive data that are authentic-looking but not genuine. This will ensure that tests run on correctly masked data provide the same outcomes as tests run on the real dataset.

 

Many regulated businesses need the protection of personally identifiable information from overexposure, and data masking is crucial in these sectors. The company can reveal data to test teams or database administrators as necessary without jeopardizing the data or breaking regulations by disguising the data. The main advantage is less security risk.

 

Data masking is challenging because any original data properties that call for particular processing must be preserved in the modified data. However, it must be sufficiently altered to prevent reverse engineering by anybody viewing the duplicate. It is possible to automate masking and guarantee the caliber of the obfuscation using commercial software solutions.

 

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What Is Data Masking?

 

Data masking is a method for producing a version of data that resembles the original in terms of structure but conceals (masks) sensitive information. The version with the concealed data can subsequently be utilized for a variety of tasks, including software testing or user training. The basic goal of data masking is to produce a useful replacement that conceals the true data.

 

Most firms have strict security measures in place to safeguard production data both when it is being used for business purposes and resting in storage. However, occasionally data is utilized by other parties or for less secure activities like testing or training. This may result in compliance breaches and put the data in danger.

 

Data masking provides a substitute that can permit information access while safeguarding sensitive data. Data masking procedures change the values of sensitive data while simulating the actual data in the same data format.

 

Character shuffle, word or character replacement, and encryption are only a few of the many methods that may ‌change data. Every approach has certain benefits. The values must always be altered, though, when masking data, making reverse engineering difficult.

 

Importance of Data Masking

 

Data masking is significant to businesses in several ways:

 

1. Reduces the risk of sensitive data exposure, assisting businesses in maintaining compliance with the General Data Protection Regulation (GDPR). Data masking gives many firms a competitive edge because of this.

 

2. Ensures that data is consistent and usable while rendering it worthless to cyberattackers.

 

3. Minimizes the dangers of data sharing through the integration of outside apps and cloud migrations.

 

4. Reduces the risks involved in outsourcing any job. Masking protects data from being exploited or stolen because most businesses only rely on trust when working with outsourced personnel.

 

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Types of Data Masking

 

Depending on your use case, there are many methods of data masking that you may apply. The most popular types of data masking among the few are static and dynamic.

 

1. Static data masking (SDM)

 

In most cases, a production database copy may be used for static data masking. SDM alters data to make it appear correct so that it may be developed, tested, and trained accurately—all without disclosing the real facts. 

 

2. Dynamic data masking (DDM)

 

DDM occurs dynamically at runtime and feeds data straight from a production system, eliminating the need to store masking information in a separate database. In order to process role-based security for applications like processing customer inquiries and managing medical information, it is largely used. In order to avoid writing the masked data back into the production system, DDM applies to read-only settings.

 

A database proxy that alters queries sent to the source database and sends the disguised data to the asking party can ‌achieve DDM. DDM eliminates the need to create a disguised database in advance, however, the application may perform poorly.

 

3. Deterministic data masking

 

Replace column data with the same value to do deterministic data masking.

 

4. On-the-fly data masking

 

Data transfers from production settings to other environments, such as test or development, result in on-the-fly data masking. A company should use on-the-fly data masking if it continuously deploys software and has extensive integrations.

 

This procedure will send just a portion of the masked data as needed because it is difficult to maintain a backup copy of the data continually.

 

5. Statistical data obfuscation

 

Production data may contain various statistical data that statistical data obscuration techniques may mask. Using a method called differential privacy, you can communicate data about trends in a data set without disclosing information about the real people who made up the data set.

 

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Techniques of Data Masking

 

The techniques for data masking are as follows: 

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Techniques of Data Masking

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1. Encryption

 

The most difficult and safe method of data masking is encryption. Here, you utilize an encryption method to hide the data and encrypt it with an encryption key.

 

For production data that must be restored to its original condition, encryption is a better option. However, as long as only allowed individuals possess the key, the data will be secure. The keys can ‌decrypt the data and examine the real data if any unauthorized entity gains access. Therefore, it is essential to handle the encryption key properly.

 

2. Scrambling

 

Scrambling is a simple masking method that hides the original material by arranging the letters and integers in an arbitrary order. This is a straightforward approach to use, but it only works with specific kinds of data, and it does not make sensitive data as safe as you might want.

 

3. Nulling out

 

By assigning a null value to a data column, nulling out hides the data so that any unauthorized user cannot view it. Another straightforward method, although the primary issues with it are that it compromises data integrity and makes using such data in testing and development more difficult. 

 

4. Substitution

 

By replacing the data with a different value, substitution masks the original value. This is one of the best data masking techniques since it keeps the data's natural appearance and texture.

 

The replacement approach applies to a variety of data formats. For instance, using a random lookup file to conceal customer names Although it might challenge to carry out, this method of data security is quite effective.

 

5. Shuffling

 

Similar to a replacement, shuffling employs the same individual masking data field but does it in a different way.

 

For instance, rearranging the columns for employee names across different employee records. Although the produced data doesn't really expose any personal information, it appears to be accurate data. Shuffled data, however, is vulnerable to reverse engineering if someone learns the technique used to shuffle the data.

 

6. Number & date variance

 

The number and data variance approach can ‌conceal crucial financial and transactional data.

 

7. Date aging

 

Based on the specified data masking policy and an allowed date range, this masking approach either raises or reduces a date field.

 

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Data Masking Best Practices

 

Data discovery is necessary before you can safeguard your data. You must be aware of the data you have and be able to differentiate between different categories of information with different levels of sensitivity. 

 

Typically, business and security specialists work together to create an extensive list of all the data components within an organization. Data masking best practices include:

 

• The security director in charge of defining the accessibility of sensitive data should keep an eye on the settings in which the data is kept and utilized. They should also choose the best concealment method for each type of data.

 

• For large businesses, it is not practical to use a single data masking strategy for all datasets. Each sort of data must be taken into account in terms of the best engineering, layout, and usage requirements.

 

• Testing the results of data veiling techniques is known as veiling. The data masking strategies must deliver the intended results, according to the QA and testing teams' assurances. The DBA must restore the database to its original, unmasked condition and use a fresh masking method with new computations if a masking strategy doesn't live up to expectations.

 

• Companies should be aware of the information that has to be secured, who is permitted to access it, which apps utilize the data, and where it is located, both in production and non-production domains, in order to execute data masking properly. While this may appear simple on paper, given the complexity of operations and the several lines of business, it may need a lot of work and has to be planned as a distinct project stage.

 

It is crucial to think about how to safeguard the dictionaries or alternate data sets that are used to obfuscate the data, as well as the algorithms that create the data. 

 

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These algorithms should be regarded as very sensitive as only authorized individuals should have access to the genuine data. The adoption of specific repeatable masking methods enables the reverse engineering of significant chunks of private data.

 

In general, data masking makes sure that the data is only visible to those who need to view it and that they only do so when they need to. It is employed to safeguard a variety of data kinds, including financial data like credit card numbers, personally identifiable information, protected health information, and intellectual property.