What is data tokenization?

As companies handle greater volumes of sensitive data each day, they also face potential data breaches that are growing more sophisticated. In addition to protecting their business and customer data, organizations must navigate an increasingly complex and evolving data ecosystem in which data moves across on-premise, cloud and third-party systems.

Tokenization is a long-term security initiative that can further improve a company's data  governance strategy that touches each line of business. We believe tokenization is a significant way for businesses to reduce data security risks in a constantly evolving threat landscape while supporting and adapting to business growth and scalability.

Data tokenization as a broad term is the process of replacing raw data with a digital representation. In data security, tokenization replaces sensitive data with randomized, nonsensitive substitutes, called tokens, that have no traceable relationship back to the original data. Tokenization removes sensitive data, such as social security numbers and bank account numbers, from an environment or system to reduce risks from data breaches. It is a popular security measure among many industries, including financial services, healthcare and education to protect data like patient records and credit card information. 

Key characteristics of tokenization include:

• Sensitive data is devalued if compromised since tokens are randomized values with no mathematical ties to the original data. 

• Tokens can be compatible with existing systems, allowing businesses to operate as normal without disruption.

• Tokens can preserve the format of the original data so that tokens can be processed across legacy systems and applications.

There’s an entire ecosystem of security methods and controls that need to be in place to ensure  companies protect their systems as well as the data itself. Additionally, as more companies move their data workloads to the cloud for benefits like highly scalable storage and computing, the need for securing data in remote storage and while in transit between environments and across systems has grown. 

Tokenization is also consistent with data minimization techniques. Data minimization means companies limit the amount of personal information that is collected or stored to minimize the risks of data breaches and to protect users’ privacy.

There are two distinct implementations to tokenization today: vaulted and vaultless tokenization. 

• Vaulted tokenization: In this more traditional method of tokenization, the mapping between tokens and the original data is stored in a secure database called a vault. The database is kept in a separate system from the tokenized data for another layer of security. When an organization needs to protect sensitive data using tokenization, the original data is sent to the vault where it is converted into a token and stored securely, usually using encryption. 

• Vaultless tokenization: Instead of storing the tokens and the original data in a vault, vaultless tokenization algorithmically generates tokens and retrieves sensitive data. This eliminates the need for a centralized database and reduces the security risk associated with maintaining a vault. Tokens can be generated at the source and sensitive data remains in the user’s environment. Since only the tokenized values are transmitted and stored, vaultless tokenization is suitable for environments such as cloud-based or third party systems where the user wants to maintain control and responsibility over the sensitive data.

In an increasingly regulated data environment, companies are using tokenization for a broad range of use cases to keep their data safe.

Tokenization is widely used in payment transactions to protect credit card and payment data by replacing them with tokens. Tokenization is particularly useful in retail and ecommerce for online or mobile transactions and point-of-sale (POS) systems, obfuscating payment details and reducing the chances of credit card fraud.

Tokenization can help enterprises comply with effective data governance. With tokenization, businesses can replace sensitive data such as Primary Account Numbers (PANs) with tokens to reduce the risk of the amount of sensitive data stored. 

Enterprises use tokenization to protect sensitive data by sending token substitutes across public cloud environments for storing and processing. Additionally, enterprises can send tokenized data to third-party systems, such as SaaS solutions, without exposing sensitive data.

The enthusiastic adoption of generative AI across industries has led to many business advantages, but also has given rise to data privacy concerns. Tokenization safeguards sensitive data used in AI, such as model training, by replacing the data with tokens. Businesses use tokenization to prevent inadvertent exposure of Personally Identifiable Information (PII) in AI training and content generation. Reducing the amount of sensitive information through tokenization also limits malicious attacks on generative AI that can lead to harmful or biased generation of content.

To protect against evolving data security threats, enterprises need solutions that provide both security and peace of mind. Whether you're mitigating risk or strengthening your defenses, tokenization is a critical layer in your security stack—helping ensure sensitive data remains protected without compromising usability. In Part 2, we’ll explore how tokenization can benefit your organization and outline best practices for implementing and maintaining a robust tokenization strategy.