Data Masking: Importance, Types, Techniques, and Best Practices

Data Masking: Importance, Types, Techniques, and Best Practices

Data masking is a reliable method for creating an authentic-looking yet fabricated rendition of the company’s data. The primary objective is to showcase information but preserve confidentiality.

It also provides a practical substitute in situations where actual data is unnecessary. The situations can be user education, sales presentations, or software testing.

Data masking involves modifying the values of the data in a consistent format. The aim is to make it indecipherable or irreversible. There are several ways to achieve this. They include character shuffling, character or word substitution, and encryption.

Importance of Data Masking

Importance of Data Masking

Here are several reasons why data masking is essential for many organizations.

Data masking addresses multiple critical risks, such as:

  1. Data loss
  2. Data exfiltration
  3. Insider threats
  4. Account compromise
  5. Insecure interfaces with third-party systems

Data masking mitigates data-related risks when adopting cloud technology. It also maintains the functional properties of the data, but is of no value to attackers. Masking ensures access to authorized users only. So while developers or testers can use it, no one else will have access to production data. Additionally, it facilitates data sanitization, by removing old obsolete data.

Data Masking Types

Several data masking types are well known to secure sensitive data.

  • Static Data Masking

Implementing static data masking procedures can generate a sanitized database version. This method modifies all confidential data. And it renders it safe to share an accurate replica of the original database.

Generally, this technique requires the creation of a backup database from the production environment. It could be created by transferring data to a secure setting, deleting extraneous data, and masking data while it is unavailable. Businesses can then transmit the safeguarded data to the intended destination.

  • Deterministic Data Masking

The process entails aligning two data sets that share the same data type. However, a particular value is consistently substituted with another.

For example, the name “John Smith” is replaced with “Jim Jameson” in all instances across a database. Although this approach is practical for many use cases, it poses a lower level of security.

  • On-the-Fly Data Masking

It is possible to mask the data on the fly. It will help to enhance security and compliance during data transfer from production systems to development or test systems.

Frequently while deploying software, creating a backup copy of the source database and applying masking may be difficult. Hence, continuous data streaming from production to multiple test environments becomes the ideal solution.

On-the-fly masking involves sending smaller subsets of masked data as required. They are then stored in the development or test environment, so non-production systems can use them.

It is essential to apply on-the-fly masking at the onset of any development project. This is to avoid any compliance and security issues.

  • Dynamic Data Masking

Unlike dynamic masking, data is not stored in a supplementary repository in the development and testing environment.

Instead, a direct stream of data from the production is processed by a different approach in the development and testing domain.

  • Data Masking Techniques

In consideration of safeguarding sensitive data, organizations use several standard data masking methods. IT specialists may utilize a wide range of techniques to ensure data protection.

  • Data Encryption

Encryption renders data worthless unless the recipient possesses the decryption key. The encryption algorithm effectively obscures the data, creating the most secure form of masking. However, implementing this process can pose a challenge. It is due to the need for advanced technology to conduct ongoing data encryption.

  • Data Scrambling

Reshuffling characters in an arbitrary sequence and substituting the original information is straightforward.

For example, a production database’s identification number, such as 76498, can be replaced with 84967 in a test database.

However, it is crucial to remember that this strategy has limitations, and its security is lower than other data masking techniques.

  • Nulling Out

Unauthorized users may observe that data is absent or labeled as “null,” diminishing its utility for developmental and evaluative objectives.

  • Value Variance

A technique used to preserve data privacy involves substituting original values with a function. This substitution can be done with a metric like a delta between a series’ minimum and maximum value.

For instance, in cases where a customer has bought multiple products, the purchase price can be conveyed by the range between the highest and lowest price paid. This method can yield valuable insights while maintaining the confidentiality of the original data set.

  • Data Substitution

To maintain privacy and confidentiality, actual data is replaced with synthetic yet authentic values. For example, original client names may be exchanged with arbitrary names sourced from a directory.

  • Data Shuffling

In the context of data masking, a technique known as shuffling is used. In this, data values within the same dataset are swapped. The process involves rearranging the data in each column using a random sequence.

A practical example would be shuffling between real customer names across multiple customer records. The result resembles realistic data but does not reveal the genuine identity of any individual or data record.

  • Pseudonymization

The EU General Data Protection Regulation (GDPR) has introduced a new term, pseudonymization. It aims to safeguard personal data through data masking, encryption, and hashing. As per the GDPR’s definition, pseudonymization refers to any technique that prevents the use of data for personal identification purposes.

This method entails eliminating direct identifiers and abstaining from utilizing multiple identifiers that, when merged, can identify an individual.

It is also necessary to store the encryption keys or related data that can potentially revert to the original data values, securely and separately.

Data Masking Best Practice

  • Determine the Project Scope

Businesses need to carry out data masking efficiently. They must possess a clear understanding of the data that necessitates safeguarding to do it, such as:

  1. Who has permission to access it
  2. Which software applications depend on it
  3. Its location across production and non-production domains

It may sound straightforward. But executing this process can prove challenging. It is because of operational complexities and multiple business units. So, it requires effort and separate planning as an essential project stage.

  • Ensure Referential Integrity

It is essential to maintain referential integrity to apply a special data masking algorithm. It is a requirement for all types of information originating from business applications. But implementing a single data masking tool in large organizations may only sometimes be workable.

Different lines of business may require unique data masking solutions. It may depend on their respective budget, IT administration practices, and regulatory requirements.

So, it is crucial to synchronize various data masking tools and practices. It is a must throughout the organization when dealing with the same data type. This approach will help avoid complications when sharing data across different business lines.

  • Secure the Data Masking Algorithms

It is essential to contemplate the measures to safeguard the data masking algorithms. It is also applicable to the alternative datasets utilized to scramble the data. Only authorized personnel ought to possess access to the actual data.

It is imperative to consider these algorithms as exceedingly sensitive. Sometimes individual acquires knowledge of the repeatable masking algorithms in use. In such cases, it may reverse sizeable amounts of confidential information.

It is an essential data masking practice. Some regulations mandate it to guarantee the separation of duties. For example, IT security personnel should determine the general methods and algorithms. Specific algorithm settings and data lists ought only to be accessible to the data owners who work in the relevant department.

Also Read: What is Data Security? Data Security Definition and Overview

Summing Up

Data masking, or data obfuscation, involves modifying data elements. They include characters or numbers to conceal the original information. The primary purpose of data masking is to generate an alternate version of sensitive data. The alternative should be difficult identify or recover, thereby safeguarding it.

A crucial feature of data masking is its ability to maintain consistency in the data across databases. At the same time, it should preserve its usability.

An all-encompassing security solution is necessary for companies. This is applicable for companies to safeguard their sensitive data through data masking. IT infrastructure and data access points need protection against advanced cyber-attacks.

This should be there, along with data masking techniques. Data masking is a crucial process several organizations use. It is to shield confidential data by obscuring its true identity.

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