Data Masking: The Core of Ensuring GDPR and other Regulatory Compliance Strategies

This article has provided an overview of data masking and its importance in ensuring compliance with GDPR and other global regulations.

Data Masking: The Core of Ensuring GDPR and other Regulatory Compliance Strategies
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Privacy is not a product up for sale but a valuable asset that preserves the integrity of every individual. That’s just one of the many triggers that led to the formulation of the GDPR and several other global regulations. With the increasing importance placed on data privacy, data masking has become necessary for organizations of all sizes to maintain the security and confidentiality of personal information.

Data masking has a mission – to protect Personally Identifiable Information (PII) and restrict access whenever possible. It anonymizes and safeguards personal and sensitive information. That’s why it applies to bank accounts, credit cards, phone numbers, and health and social security details. No Personally Identifiable Information (PII) is visible during a data breach. You can also set additional security access rules within your organization.


What is Data Masking?


Data masking, as we know, is a technique used to protect sensitive data by replacing it with fictitious but realistic data. It protects personal data in compliance with the General Data Protection Regulation (GDPR) by ensuring that data breaches do not reveal sensitive information about individuals.

Since data masking is an integral component of the data protection strategy, it applies to various data types such as files, backups, and databases. It works closely with encryption, access controls, monitoring, and others to ensure end-to-end compliance with GDPR and other regulations.


Why do we need this for GDPR and other Regulations?


Despite masking’s proven capability in eliminating the exposure of sensitive data, a lot of enterprises are not following the guidelines and stand at the risk of breach. The most popular case is related to a clothing retailer, H&M, that had to incur a fine of 35 million Euros for violating the GDPR norms. It was found that the management had access to sensitive data such as an individual’s religious beliefs, personal issues, etc. That’s what GDPR tries to avoid and that’s why data masking is essential.

However, heavily regulated industries such as BFSI and healthcare are already implementing data masking to comply with privacy regulations. These include the Payment Card Industry Data Security Standard (PCI DSS), and the Health Insurance Portability and Accountability Act (HIPAA).

The implementation of Europe's GDPR in 2018 has sparked a global trend of privacy laws, with jurisdictions such as California, Brazil, and Southeast Asia introducing laws such as CCPA and CCPR, LGPD, and PDPA, respectively, to protect personal data.


Data masking can provide several benefits for regulatory compliance, including


  • Protecting sensitive data: Data masking can protect sensitive data, such as personal information, by replacing it with fictitious but realistic data. This can prevent unauthorized access or accidental exposure of sensitive data.
  • Compliance with regulations: Data masking can be used to anonymize personal data, which can help organizations comply with regulations such as the General Data Protection Regulation (GDPR) and other data privacy laws.
  • Auditing and compliance: Data masking can provide an auditable trail of who has accessed sensitive data, which can help organizations demonstrate compliance with regulatory requirements.
  • Data Governance: Data masking can be used as a data governance tool; organizations can ensure that sensitive data is only used for the intended purposes and by authorized personnel.


Key Data Masking Practices for GDPR


Data Minimization 


Data minimization in data masking refers to only masking the minimum amount necessary to protect sensitive information while still allowing the data to be used for its intended purpose. This can help organizations balance the need to protect sensitive data with the need to make use of the data for business purposes.

For example, an organization may only need to mask the last four digits of a credit card number to protect sensitive information while allowing the data to be used for financial transactions. Similarly, in personal data, only masking specific fields like name and address while keeping the other fields like gender and date of birth can be sufficient for specific use cases.




Pseudonymisation uses pseudonyms to replace the identifying information of the users and thus protect their privacy. This is useful in ensuring compliance with regulations such as the General Data Protection Regulation (GDPR) by ensuring that data breaches do not reveal sensitive information about individuals.

This data masking technique replaces personal identifiers such as name, address, and social security number with a unique pseudonym while keeping other non-sensitive attributes such as gender and date of birth intact. The pseudonyms can be generated using cryptographic techniques, such as hashing or encryption, to ensure that the original personal data cannot be reconstructed.

It also aligns with the regulation's requirements for security and safe data processing for scientific, historical, and statistical purposes (analytics). It's a valuable tool in ensuring compliance with the GDPR's data protection by design principle.

You can optimize your DevOps function. For DevOps, data masking enables realistic yet secured fictitious data for testing. This is particularly beneficial for organizations that rely on internal or third-party developers as it ensures security and minimizes delays in the DevOps process. Data masking allows you to test your customers' data while maintaining their privacy.


Data Masking with Data Products for GDPR and other Regulations


Treating data as products and using them to implement masking techniques have a lot of benefits. In 2022, many data fabrics and product platforms got popular for their innovative approach. For example, K2view performs data masking at the business entity level, ensuring consistency and completeness while preserving referential integrity.

To ensure maximum security, each business entity's data is managed within its Micro-Database, protected by its 256-bit encryption key. Additionally, the personally identifiable information (PII) within the Micro-Database is masked in real-time, following predefined business rules, providing an added layer of protection.


End Note


Implementing data masking techniques can help organizations avoid hefty fines and damage to their reputation. However, it's important to note that data masking alone is insufficient to achieve GDPR compliance and should be used in conjunction with other security measures.

Yash Mehta is an internationally recognized IoT, M2M and Big Data technology expert. He has written a number of widely acknowledged articles on Data Science, IoT, Business Innovation and Cognitive intelligence. He is the founder of a data insight platform called Expersight. His articles have been featured in the most authoritative publications and awarded as one of the most innovative and influential works in the connected technology industry by the IBM and Cisco IoT departments.