Common Data Masking Techniques and How to Implement them?

What is Data Masking?

Data Masking is creating a false version of the original data for businesses to retain their authentic capabilities. Data masking is also known as Data Obfuscation. Moreover, the agenda is to put forth an operational solution that hides the sensitive data and, in turn, offers a replica of the structure. Further, data masking functions help during various software testing, demos, or training users where there is no need for the original data.

Data masking also maintains a basic data structure and only falsifies its values. Moreover, the false data is impossible to reverse engineer or decrypt for information. Hence, techniques like shuffling, replacing, deleting, substitution, encryption, etc.   

Which type of Data requires Data Masking Techniques?

• Personally Identifiable Information (PII): PII refers to the data that identifies and relates to certain individuals. Moreover, information like name, address, passport, driver’s license, etc.  

• Protected Health Information (PHI):  Further, the healthcare industry includes confidential and sensitive information. Moreover, providers and institutions collect data to identify and offer proper care. PHI also includes insurance details, demography, lab results, etc. 

• Payment Card Information: Moreover, the Payment Card Industry Data Security Standard (PCIDSS) compels merchants to monitor credit and debit card transactions and information. Hence, it helps secure and protect the cardholder’s confidential data.

• Intellectual Property (IP): Intellectual Property signifies the data like inventions, business plans, designs, strategies, etc. Moreover, these data sets are of high value to any business and require protection. 

Types of Data Masking

• Static Data Masking (SDM): 

Static data masking refers to the work on the copy of production datasets. Moreover, it alters the data to look more accurate to develop, test, and train employees without disclosing the original data. 

• Dynamic Data Masking (DDM):

DDM is a process that occurs dynamically during real-time execution and masks the data to ensure it is not saved in any other database. Moreover, it essentially processes security for applications. Hence, it only implies “read-only” situations that prevent writing the false data into real-time operations.

• Deterministic Data Masking:

Deterministic data masking refers to replacing the column data with similar values. For instance, a column with first names in the data sets may include multiple tables that state first names. Hence, here if the name is “Harper”, it is replaced with “Bailey” due to the masking. As a result, whenever users process the masking provides the same result.

• On-the-fly Data Masking:

OTF data masking occurs when there are data transfers from creation environments to different environments. Moreover, it is suitable for businesses that execute software constantly and include heavy integrations. 

• Statistical Data Obfuscation:

Here, the statistical information is masked with the statistical data obfuscation process. Moreover, techniques like differential privacy help share information about patterns. Further, it only discloses the patterns and masquerades the original data from the sets.

Importance of using Data Masking Techniques

• Data masking helps resolve various crucial threats and attempts that lead to data loss, breach, exfiltration, and other malpractices. Hence, businesses can prevent these attempts with data masking techniques and safeguard their data.
• Moreover, it also depletes any risks that come along with cloud adoption.
• It also helps make the data useless for attackers and hackers while retaining its functional aspects. 
• It also enables safe data transfers to specific users with authentications without releasing the data.
• Further, it helps in data sanitization where various file deletion leaves traces of the data.

Challenges in Data Masking

• Firstly, the challenge occurs in identifying confidential data and PII requires additional protection.
• Further, it needs to resolve various identities to maintain similar values across systems.
• There is also a requirement to comply with the data according to governance policies.
• Moreover, it must help with real-time access and maximum data extraction scalability.
• It also faces issues in regards to managing large volumes of unstructured data.

Benefits of Data Masking

• Firstly, data masking is a pivotal process run by many regulations and compliances like HIPAA. Hence, it maintains the confidentiality of the data for protection and security.
• It also maintains the integrity and structure of the data sets.
• Moreover, businesses can enable access for developers and testers without uncovering the information.
• It also reduces the security risk while processing and analyzing the data and its results.

Here are the Top Data Masking Techniques for Businesses

• Data Pseudonymization:

Firstly, data pseudonymization replaces the data sets with information like name, email, contact information, etc. with various pseudonyms and information. Moreover, the process is reversible and it de-identifies the data and re-identifies it when required.

• Data Anonymization:

Further, the technique of data anonymization refers to the process of encoding the data. As a result, it helps protect the privacy of individuals and their activity while preserving the credibility of a business.

• Lookup Substitution:

Moreover, with lookup substitution, the creation of the datasets are added with a lookup table that offers alternative details to protect the original data. Hence, it enables businesses to utilize realistic data while running tests without compromising the original.

• Encryption:

Data encryption enables encrypting sensitive data that are accessible using a specific passcode, or keyword. Moreover, without the passcode the data is unreadable. Hence, only authorized users can access the decrypted data.

• Redaction:

Further, the technique of redaction refers to using redundant or old data sets to replace general details while developing and testing. It also retains realistic data with similar aspects to the original.

• Averaging:

Averaging is replacing values with various averages or aggregates to secure sensitive data. Moreover, it replaces calculable original data to mask and secure the information.