What Is Data Tokenization?
Why Is Data Tokenization Important for Data Security?
When Should I Use Data Tokenization? Top Tokenization Use Cases
What’s the Difference Between Tokenization and Encryption?
Applying Data Tokenization for Secure Analytics
At a time when data is one of the most important assets companies can leverage, ensuring that it remains secure is critical. Data security and governance consistently appear on lists of data leaders’ greatest challenges, and data leaks and breaches have simultaneously become more frequent.
To mitigate threats to data privacy, organizations are increasingly relying on data tokenization, a process that involves swapping out sensitive data, such as a customer’s social security or bank account number, with a randomly generated data string called a token. Importantly, tokens don’t have any inherent meaning, nor can they be reverse-engineered to reveal the original data they represent. Only the system that created the token can be used to obtain the original data it represents through a process known as de-tokenization.
This blog takes a closer look at what data tokenization is and how it works. We’ll also explore some common data tokenization use cases, as well as how it differs from encryption.
A survey of data professionals found that 75% of organizations collect and store sensitive data, which they are currently using or have plans to use. Tokenization is a way of protecting that data by replacing it with tokens that act as surrogates for the actual information. A customer’s 16-digit credit card number, for example, might be replaced with a random string of numbers, letters, or symbols. This tokenization process would make it impossible for a potential attacker to exploit the customer’s credit card number, thus making any online payments infinitely more secure.
When companies adopt tokenization, they’re able to use their data just as they always have while also enjoying the added benefit of being safeguarded against the risks typically associated with storing sensitive data. This makes them far less vulnerable in the event of a data breach, and in a much better position to remain compliant with an array of ever-evolving data compliance laws and regulations.
Data tokenization helps organizations strike the right balance between realizing the full value of their data while still keeping it secure. In highly regulated industries, such as healthcare and financial services, it’s an effective way of deriving much-needed information without increasing the surface area for risk. At the same time, using data tokenization can help earn customers’ trust by giving them the peace of mind that comes with knowing their personally identifiable information (PII) will not fall into the wrong hands.
In addition to making tasks like online payments more secure, data tokenization can help protect data in a wide range of scenarios. These include:
The Payment Card Industry Security Standard (PCI DSS) applies to any organization that accepts, processes, stores, or transmits credit card information in order to ensure that data is handled in a secure manner. Data tokenization is used to satisfy this standard because tokens aren’t typically subject to compliance requirements such as PCI DSS 3.2.1, provided there’s sufficient separation of the tokenization implementation and the applications using the tokens. Using tokenization can therefore save organizations considerable time and administrative overhead.
Sharing tokenized data with third parties rather than sensitive data eliminates the risks typically associated with giving external parties control of such information. Tokenization also allows the organizations responsible for that data to sidestep any compliance requirements that may apply when data is shared across different jurisdictions and environments, including data localization laws like the GDPR.
The principle of least privilege is meant to ensure that people only have access to the specific data they need to complete a particular task. Tokenization can be used to achieve least-privileged access to sensitive data. In cases where data is co-mingled in a data lake, data mesh, or other repository, tokenization can help ensure that only those people with the appropriate access can perform the de-tokenization process to access sensitive data.
Tokenization is also helpful for allowing sensitive data to be used for other purposes, including data analysis, and for mitigating threats that may have been identified through a risk assessment process or threat model.
[Read More] How to Design and Implement a Governance, Risk, and Compliance Framework for Data Analytics
Tokenization and encryption are often referred to interchangeably. Both are data obfuscation techniques that help secure information in transit and at rest. Though very similar, it’s important to understand the differences between these approaches to data privacy.
While tokenization replaces data with a randomly generated token value, encryption converts plaintext information into a non-readable form, called ciphertext, using an encryption algorithm and key.
Deciding which approach is right for you depends on your organization’s needs. Tokenization, for example, is great for organizations that want to stay compliant and minimize their obligations under PCI DSS. Meanwhile, encryption is ideal for exchanging sensitive information with those who have an encryption key. As remote work has exploded in recent years and data is increasingly accessed from many different locations, encryption is a common method for safeguarding against data breaches or leaks. ATMs also often use encryption technology to ensure information remains secure in transit. This makes it a great choice for organizations that need to encrypt large volumes of data.
As organizations collect and store more data for analytics, particularly in an increasingly regulated environment, tokenization will be central to ensuring data security and compliance. However, the speed at which organizations need to enable data access and the complexity of today’s cloud environments could make implementing it easier said than done – without the right tools.
The Immuta Data Security Platform helps streamline and scale this process through powerful external masking capabilities, including data tokenization. Organizations are able to tokenize data on ingest, and Immuta de-tokenizes it at query runtime using that organization’s algorithms or keys defined by Immuta policies. Additionally, Immuta provides a range of other dynamic data masking capabilities, including advanced privacy enhancing technologies (PETs), that are automatically enforced using attribute-based access control and can be monitored and audited on-demand.
To see for yourself how easy it is to implement data access control using Immuta, check out our walkthrough demo.
To see how easy it is to start building policies with Immuta, check out our self-guided demo.
