Top Data Masking Techniques

Bineesh Mathew | April 13, 2022 | 135 views

Top Data Masking Techniques

“Data masking is the process of systematically transforming confidential data elements such as trade secrets and personally identifying information (PII) into realistic but fictionalized values. Masking enables receipts of the data to use “productionlike” information while ensuring compliance with privacy protection rules.”

                                                                            -IBM

Businesses must ensure the minimum use of private data, considering rising cyber threats and implementing data privacy legislation such as the CCPA in the U.S. or GDPR in the EU. Data masking allows firms to limit private data while testing their systems with data that looks similar to actual data.

In 2020, Data masking to secure sensitive data was expected to cost $4.24 million. It provided substantial incentives for organizations to invest in information security solutions such as data masking. Data masking is a must-have option for businesses that want to follow the GDPR or use real data in a testing environment.

Every year, the number of data breach cases is rising. So, businesses must upgrade their data security methods. The necessity for data masking is increasing due to the following factors:

  • Organizations, for non-production reasons, need a copy of production data. The non-production reasons can be business analytics modeling and application testing.
  • The data privacy policy of an organization is threatened by people inside. So, firms should be careful when granting access to insider staff.

According to the Insider Data Breach survey in 2019:

  1. 79% of CIOs think that employees put data at risk accidentally, while 61% believe they do it maliciously.
  2. 95% of employees believe that insider cybersecurity threats are harmful to their organizations.
Businesses must keep changing and upgrading their data protection processes under GDPR and CCPA.


Challenges of Data Masking

The data masking process has significant challenges, such as:

  • Generating transformed data while retaining the features of the original data.
  • Keep demographic data as authentic as possible.
  • Achieve high throughput and low latency without compromising the user experience.
  • The integration should be smooth, without altering the applications or data.
  • Data masking protects your data from both external and internal threats. As a result, while obfuscating, some industry-defined best practices must be observed.


Top Data Masking Techniques


Character Scrambling

This is the simplest technique in which the characters are altered randomly or in a jumbled manner. This process can't be reversed to the original form from the scrambled data.


Shuffling

If you need to preserve uniqueness when masking values, shuffle the data randomly to a different column or row to protect the original values. It is a method in which original data is replaced with authentic-looking data. For example, the actual salaries will be displayed in the salary table, but it will not be disclosed which salary goes to which individual. Larger datasets are best suited for this strategy.


Substitution

Businesses use the substitution strategy to replace original data with random data from a given or customized lookup file. This is an excellent approach to hiding data because it keeps the original appearance.


Data Anonymization

It is a technique of data masking in which users with an encryption key can access the data. This is considered the most complex masking technique. This form of masking is suitable if data is required to return to its original state. The goal is to secure users' personal information while maintaining the integrity of the disguised data.


Averaging

You can replace all the numbers in the table with the average value to reflect sensitive data in terms of averages or aggregates, but not individually. For example, there is a table has the salaries of employees mentioned. In this case, you can hide the actual individual salaries by replacing them with the average salary, so the overall column reflects the combined salary's real value.


Redaction

If sensitive data isn't required for QA or development, generic values can be substituted in the development and testing environment. However, there is no real data similar to the original in this scenario.


How to Implement Data Masking Effectively

Before transferring sensitive data to the testing environment, make sure you have it all in the enterprise's database. Next, recognize your sensitive data and select the appropriate data masking technique. When you're done, use methods that don’t allow data to be restored to its original state.

Apart from this, you can have a step-by-step process of data masking, such as:

  • Data discovery
  • Survey of circumstances
  • Veiling actualization
  • Veiling testing

Companies can also use the latest practical data masking tools. Some of the other well-known data masking tools are:

  • IBM Infosphere Optim
  • Mentis
  • CA Test Data Manager
  • HPE SecureData Enterprise
  • Dataguise Privacy on Demand Platform
  • Oracle Advanced Security (for DDM)


Summing Up

There are many database security options. Data masking is important because it is at the heart of an organization and could be a goldmine for employees and hackers who want to make money on the black market.

Data masking is just one of the steps that businesses must take to avoid becoming subject to class action lawsuits, negative press, and cautionary tales for years to come.


Frequently Asked Questions


What does data masking mean?

Data masking is a data security approach that involves copying a dataset but masking critical data. This duplicate data is then used for testing or training purposes, instead of the original data.


What is an example of data masking?

Masking customer names with a random lookup file is an example of data masking.


What are some of the types of data masking?

Some types of data masking are static data masking (SDM), dynamic data masking (DDM), deterministic data masking, on-the-fly data masking, and statistical data obfuscation.

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