Encryption vs. Tokenization vs. Masking

The adoption of technology is a must to keep data safe throughout each stage of its life cycle. Organizations can choose from a plethora of data protection methods such as encryption, tokenization, and masking (to name a few) that they often face difficulty in deciding the right approach. Choosing the correct path requires the decision-maker to be knowledgeable about the given choices. So, let’s break down the following three techniques, the benefits of each and which solution to opt:


Encryption works by encoding the original data or plaintext with the help of sophisticated algorithms that convert it to unreadable text or ciphertext; a decryption key would be needed to revert to a readable format. Encryption is best suited for unstructured fields or databases that aren’t exchanged regularly or stored in multiple systems. It is used to protect sensitive data such as payment card information (PCI), personally identifiable information (PII), financial account numbers, and more.


Tokenization is similar to encryption, the only difference being that a random generated alphanumeric value, called a token, replaces the original value, whereas in encryption algorithms are applied on plaintext to create ciphertext. In tokenization, the token server stores the relationships between the original and the token values. When a user application needs the original data, the tokenization system looks up the token value to retrieve it. This method is mostly used to protect sensitive data in payment processing systems, such as credit card information.


Masking has various approaches ranging from simple to complex based on the organization’s use case. A simple method is to replace the real data with null or constant values. A slightly sophisticated approach would be to mask the data in a way that retains the identity of the original data to preserve its analytical value. This approach ensures the efficient use of masked data for analysis without the fear of leaking private information.

Broadly speaking, there are two types of masking methods. Permanent scrambling of data which cannot be retrieved once masked is called Static Data Masking (SDM). Masking can also be used to control access to sensitive data based on who the user is. This method, known as Dynamic Data Masking (DDM), allows only authorized users to view the original data, whereas the masked data is shown to unauthorized users. Masking is used to secure non-production and production environments to allow for testing or quality assurance requirements and user-based access without the risk of sensitive data disclosure.


Encryption, tokenization, and masking have been around for a long time now, all having their use-cases. While encryption and tokenization are used to secure data at rest and data in motion, masking is especially beneficial for data in use. When data is continually used for business purposes such as testing and development, encryption or tokenization becomes a complicated process. The user needs to use a key to decrypt the ciphertext or use the token value to retrieve the real data many times to not risk disclosure of sensitive information. Masking addresses this issue wherein the masked data retains the characteristics of the original data, meaning it resembles the original data but is still fictitious. Hence, it is functional for business use cases without compromising sensitive data. Overall, the matter of which solution to opt depends on the organization’s need. Depending on the use case, a combination of these technologies can also end up being the best way to go about it.