No one can deny the value of data for today’s organizations. With the ongoing rise of data breaches and cyber attacks, it is increasingly essential for organizations and government agencies to protect sensitive data from unauthorized access, use, disclosure, modification, or destruction. Data security is the practice of implementing measures to ensure the confidentiality, integrity, and availability of data to the appropriate end users.
There are many techniques used in data security. In this article, we’ll focus on data privacy and two of the most popular approaches in protecting sensitive data: data masking and tokenization. At their essence, these are both techniques for generating fake data, but they are achieved in distinct, technically complex ways, and it is essential to understand their differences in order to choose the right approach for your organization.
Data masking is a data transformation method used to protect sensitive data by replacing it with a non-sensitive substitute. Often the goal of data masking is to allow the use of realistic test or demo data for development, testing, and training purposes while protecting the privacy of the sensitive data on which it is based.
Data masking can be done in a variety of ways, both in terms of the high-level approach determined by where the data lives and how the end user needs to interact with it, and in terms of the entity-level transformations applied to de-identify the data.
Briefly, the high-level approaches include:
Within each of these high-level approaches, a variety of transformation techniques can be applied to the data. Some examples include:
By identifying the best high-level masking approach for your use case and using a combination of these data masking techniques within your approach, organizations can ensure that their sensitive data is protected from unauthorized access, while also maximizing their teams’ productivity.
When implemented effectively, data masking provides a wealth of advantages, including:
The caveats and potential disadvantages of data masking include:
Tokenization is a technique used to protect sensitive data by replacing it with a non-sensitive substitute called a token. The token represents the original data, but it does not reveal any sensitive information. The goal of tokenization is to protect sensitive data while allowing authorized users to access and process the tokenized data. Tokenization is often used in the context of analysis, when the statistics of the data are important to preserve but the values need not look like real-world values.
Tokenization can also be performed in a way that is format-preserving. This technique preserves the format and length of the original data while replacing it with a token. It is widely used in the financial sector, e-commerce, and other industries where sensitive data is transmitted and stored. By preserving the format of the data, it ensures that the data can be easily processed and used by the systems that require it, while at the same time protecting it from unauthorized access and theft.
Tokenization has several advantages, including:
On the flip side, tokenization also has some disadvantages worth mentioning:
As outlined above, data masking and tokenization are two popular techniques used in data security, but they serve different purposes. Data masking is used to protect sensitive data while allowing the use of realistic test or demo data, while tokenization is used to protect sensitive data while allowing authorized users to access and process the tokenized data, for example, for use in analytics.
When comparing tokenization versus data masking, their core differences can be found in the techniques and processes they use. Data masking replaces sensitive data with a non-sensitive substitute that may look as realistic as the original, while tokenization replaces sensitive data with a token that is not intended to resemble a real-world value. Data masking can be done in an extensive variety of ways, including redaction, scrambling, substitution, and encryption, while tokenization is achieved by way of a narrower scope of approaches, including cryptographic methods and format-preserving tokenization.
Both data masking and tokenization play a vital role in data security and regulatory compliance, as they are fundamental approaches to protecting sensitive data. They help organizations comply with data privacy regulations, such as GDPR, CCPA, HIPAA, and PCI. They can be used in combination to meet the differing needs of an organization’s teams in achieving the necessary level of data protection.
Choosing between data masking and tokenization depends on your organization's specific needs and use case. If your organization needs to preserve both the privacy and the utility of its data, while granting access to software development, customer success, and sales teams, data masking is likely the better choice. For example, if your organization has a SQL database that contains PII with real-world eccentricities you need to preserve in your lower environments, data masking might solve your use case. The variety of approaches available by way of data masking enables shaping your masked data based on the specific qualities of each data type. It offers greater flexibility and control over the look and feel of the data, allowing you to achieve the right balance of privacy and utility, which is particularly useful when crafting test data for use in software development and testing.
If, on the other hand, your organization needs to protect sensitive data while allowing authorized users to access and process the protected data for use in analytics, tokenization may be a better choice. Tokenization not only allows you to preserve the statistics of your real-world data, it provides a secure way to store your data long-term, beyond the limitations for storing your original data dictated by regulations like GDPR. If long-term data storage and analytics are your goal, tokenization is likely your solution.
The Tonic test data platform is a modern solution built for today's engineering organizations, for their complex data ecosystems, and for the CI/CD workflows that require realistic, secure test data in order to run effectively. It offers both data masking and tokenization capabilities within its versatile approaches to data transformation. To learn more, explore our product pages, or connect with our team.