Data Tokenization: Use Cases and Best Practices

Ian Tick

Ian Tick

Head of Content, K2View

There’s a lot of buzz about data tokenization, and for good reason. It’s one of the most effective methods of protecting sensitive data, and complying with data privacy laws.
Table of Contents

Why Data Tokenization
Data Tokenization, Defined
Data Tokenization Use Cases
Data Tokenization vs Data Encryption
Data Tokenization Solution Considerations
Data Tokenization Based on Data Products

Why Data Tokenization

If you work with data, you’ve probably heard a lot about data tokenization.

For one thing, it’s the go-to for financial and healthcare institutions, which face hefty requirements for securing personally identifiable information (PII).

However, as enterprises, across all sectors, look for ways to fortify their cybersecurity posture and improve compliance, data tokenization is becoming more common.

This is because data tokenization provides a scalable, standardized, and safe way for organizations to deal with sensitive data while reducing their audit scope. It enables a more secure and cost-effective approach to protecting data than many other methods, and enables operational and analytical workloads without interruptions or interference.

As enterprises generate more and more data, and as requirements for protecting consumer privacy and sensitive data get tougher, tokenization will be a staple in enterprises’ data security toolbox.

In this article, we’ll take a closer look at data tokenization and cover key considerations for organizations that are seeking to implement a data tokenization solution. Read on to learn about 6 key use cases and 4 questions to consider when looking for data tokenization solutions.

Data Tokenization, Defined

Like data masking, data tokenization is a method of data obfuscation - obscuring the meaning of sensitive data to make it usable in accordance with compliance standards and keep it secure in the event of a data breach. Unlike data masking tools, it irreversibly replaces sensitive data with a non-sensitive substitute, called a token, which has no exploitable value.

When data is tokenized, it replaces sensitive data in databases, data repositories, and internal systems with non-sensitive data elements, such as a randomized string of data, which don’t have any exploitable meaning. The original sensitive data is often stored in a centralized token vault outside of the organization’s IT environment.

Although tokens themselves don’t have any value, they retain certain attributes of the original data, such as the format or length, to ensure that the business applications and analytical workloads that depend on the data continue to operate uninterrupted.

Common examples of data that gets tokenized are Social Security Numbers, bank account numbers, credit card numbers, and personal health information.

Data Tokenization

Social Security, bank account, credit card, and
driver’s license numbers are typically tokenized.

Data Tokenization Use Cases

Here’s a brief description of 6 key data tokenization use cases.

  1. Reduce compliance scope

    Data tokenization allows you to reduce the scope of data subject to compliance requirements since tokens replace data irreversibly. For example, replacing Primary Account Number (PAN) data that is stored within organizations’ IT environments with tokenized data leaves a smaller data footprint, simplifying PCI DSS compliance.

  2. Manage access to data

    Tokenization helps you fortify access controls to sensitive data by preventing those without appropriate privileges, from performing detokenization. For example, when sensitive data is stored in a central repository, such as a data lake or data warehouse, tokenization helps ensure that only authorized data consumers can detokenize sensitive data.

  3. Fortify supply chain security

    Many organizations today work with third-party software, vendors, and service providers that need access to sensitive data. Tokenization helps organizations minimize the risk of a data breach stemming from external providers by keeping sensitive data out of those environments.

  4. Simplify compliance of data warehouses and lakes

    Centralized data repositories like data lakes and data warehouses store data from multiple sources in both structure and unstructured formats. This makes the act of demonstrating data protection controls more difficult from a compliance standpoint. When ingesting sensitive data into the repository is necessary, tokenization allows you to keep original PII out of data lakes and warehouses, which, in turn, reduces compliance implications.

  5. Allow sensitive data to be used for business analytics

    Business intelligence and other kinds of analytical workloads are integral to every business unit, and there is often a need to perform analytics on sensitive data. By tokenizing this data, you can protect sensitive data while allowing other applications and processes to run analytics.

    Data tokenization protects real data from being identified during analytics.

    Data tokenization protects real data from being identified during analytics.

  6. Improve overall security posture

    Finally, data tokenization supports an overall stronger cybersecurity posture by protecting sensitive data from malicious attackers, as well as accidental insider attacks, which accounted for 60% of all data breaches in 2020. By replacing PII with randomized, non-exploitable data elements, you can maintain its full business utility while actually minimizing risk.

Data Tokenization vs Data Encryption

Encryption is a commonly used method for obfuscating data. It involves transforming sensitive data into a non-readable form called ciphertext using an algorithm . The algorithm, as well as an encryption key, are required to decrypt the information and retrieve the original data.

Malicious actors can use a variety of techniques to acquire the key, such as social engineering or brute computing force. As a result, the level of protection during the encryption process relies on the complexity of the algorithm used to encrypt it. However, all encrypted data can ultimately be broken.

Another pitfall of encryption is that ciphertext rarely retains the same format as the original data, which could limit organizations’ ability to perform analytics on it, or require analytics apps to adapt to the new format.

Unlike encryption, data tokenization cannot be reversed; tokenization permanently substitutes the data with a meaningless placeholder, and original data is kept in a separate location outside of the IT environment. That means that if a data breach within your IT systems occurs, a hacker cannot access your data, even if they get a hold of the tokens.

Data Tokenization Solution Considerations

If you’re ready to implement a data tokenization solution, consider these 4 questions before you start evaluating solutions.

  1. What are your primary business requirements?

    The most significant consideration—and the one to start with—is defining the business problem that needs to be resolved. After all, the ROI and overall success of a solution is based on its ability to fulfill the business need for which it was purchased. The most common needs are improving cybersecurity posture and making it easier to comply with data privacy regulations, such as Payment Card Industry Data Security Standard (PCI DSS). Vendors vary in their offerings for other types of PII, such as Social Security numbers or medical data.

    60-3

    Maintaining medical privacy is just important as
    protecting personal identity or financial information

  2. Where is your sensitive data?

    The next step is identifying which systems, platforms, apps, and databases store sensitive data that should be replaced with tokens. It’s also important to understand how this data flows, as data in transit is also vulnerable.

  3. What are your system/token requirements?

    What are the specific requirements for integrating a data tokenization solution to your database and apps? Consider what type of database you use, what language your apps are written in, the degree of distribution of apps and data centers, and how you authenticate users. From there, you can determine whether single-use or multi-use tokens are necessary, and if tokens can be formatted to meet required business use.

  4. Should you custom-build a solution in-house or purchase a commercial product?

    After you know your business needs and requirements for apps, integrations, and tokens, you’ll have a clearer understanding of which vendors can offer you value.

    Although organizations can, at times, build their own data tokenization solutions internally, tackling this need in-house usually adds greater strain to data engineers and data scientists, who are already stretched thin. Most of the time, the benefits of purchasing a customizable solution, receiving tailored customer support, and alleviating data teams of this task outweighs the costs.

Data Tokenization Using Data Products

The ideal data tokenization solution relies on a data product approach. A data product is a reusable data asset that provisions a complete dataset for a specific business entity (such as a customer, vendor, credit card, store, payment, or claim). Data products may be used to drive operational and analytical workloads, and may be deployed in a data mesh or a data fabric architecture.

Unlike conventional data tokenization solutions, which store all sensitive business data in one centralized data vault, a data product product platform distributes each business entity’s sensitive data into its won encrypted and tokenized Micro-Database ("micro-vault"), one for each business entity. This dramatically reduces the chances of a breach, while making compliance easier to manage. 


Data products tokenize data in real time (for operational use cases), or in batches (for analytical workloads). They are format-preserving and retain data integrity across systems to enable continued business operations.

Highly flexible and configurable, data products represent the most advanced approach to data tokenization today.

When it comes to tokenization, take a data product approach to enhance protection and reduce costs