What Is Data Tokenization?

Data tokenization replaces sensitive data with non-sensitive “tokens” to protect it against potential exposure. These placeholders can be used in insecure environments that don’t require access to the real data, while the database mapping tokens to real data is kept in a secure environment. Tokens may be wholly random or designed to preserve the format of the real data, like an address, phone number, or government ID number.

Tokenization is one approach that organizations can adopt to reduce their risk of data breaches and regulatory non-compliance. By replacing sensitive data with a token anywhere the real data isn’t needed, an organization eliminates the risk of a data breach if that system is compromised by an attacker. Additionally, systems with no access to real, sensitive data may lie outside of the scope of compliance audits, making compliance simpler and cheaper.

How Data Tokenization Works

Tokenization is like giving a code name to a person or thing. Instead of using their real name, they’re referred to as Agent Blue in all communication and documentation. This way, an eavesdropper doesn’t learn the person’s real identity.

Tokenization systems maintain a central, secure vault that protects the sensitive mapping of the real data to the placeholders. When sensitive data enters the system, it is sent to the tokenization system, which generates a token and stores the mapping of tokens to real data in the vault.

These tokens can come in various formats. For example, an organization may generate wholly random tokens of a fixed length. Alternatively, tokenization can be format-preserving, creating tokens that look like the real data. For example, the address 123 Main St may be tokenized as 385 W Elm Ave. Crucially, there is no way to reverse the scheme and retrieve the non-tokenized value without access to the secure vault.

Once tokenization is complete, the token is used in place of the real data anywhere where the real data isn’t needed. Since tokens are unique, they can be used to uniquely identify and track a particular account or record, but the information provided is all incorrect. However, if the real data is needed, it can be retrieved from the vault. For example, the only systems that might have access to a customer’s real address are those that handle billing and shipping.

Data Tokenization vs Encryption vs Masking

Companies can implement various controls to manage the risk that sensitive data may be exposed or abused. Some of the most common include:

• Tokenization: Tokenization replaces sensitive data with a unique, non-sensitive token, while storing the mapping between them in a secure vault. Tokens can preserve the format of the tokenized data, which can be useful if a system expects data of a particular format. In this approach, the token vault is a single point of failure, revealing all of the sensitive data if compromised.
• Encryption: Encryption algorithms scramble data in a way that is irreversible without access to the decryption key. With encryption, access to the decryption keys is controlled to secure access to the encrypted data.
• Masking: Masking replaces part of the sensitive data with other characters and is irreversible. For example, many systems will only show the last four digits of a credit card number, replacing the rest with asterisks.

Comparison of Data Protection Methods

Use Cases & Industries

Tokenization is a common tool for simplifying compliance with regulatory requirements. PCI DSS, HIPAA, GDPR, and similar laws implement strict requirements for securing various types of sensitive data. Tokenization is useful in these contexts because tokens don’t need to be secured at the same level as the real data as long as the vault remains secure.

For example, medical studies are a prime example of how tokenization is used regularly. In these studies, doctors may not know the identities of the patients receiving the drug under trial or a placebo to stop them from accidentally biasing the results. In this scenario, patients would be assigned an identifier (e.g., a token) that uniquely identifies them without allowing the doctors to know their real identity or access their medical records.

Tokenization may also be used in contexts where certain systems aren’t secure and trusted enough to hold real data. For example, point-of-sale (POS) terminals or cloud infrastructure might use tokens to replace sensitive data when they don’t require access to the real information.

Benefits, Risks & Limitations of Data Tokenization

Tokenization is a useful tool for data security for various reasons, including:

• Reduced Exposure: Tokenization uses placeholders to represent sensitive data on systems that don’t need access to it. This reduces the risk of data breaches since systems can’t expose data that they don’t have.
• Simplified Compliance: Regulations mandate security controls and audits to protect sensitive information. Tokenization decreases the scope of compliance by limiting the systems with access to sensitive data.
• Format Preservation: Tokenized data can preserve the format of the underlying data. This makes it usable with systems that expect data in a particular format.

However, tokenization isn’t a perfect solution. It has several limitations, including:

• Vault Centralization: Tokenization relies on a secure vault to protect the mappings from the real data to the associated tokens. If this vault is compromised, then the sensitive data is exposed.
• Vendor Lock-In: Tokenization requires the ability to tokenize or look up data as needed. If this process relies on a particular vendor solution, this introduces the risk of vendor lock-in.
• Scalability and Reliability: The secure vault is a single point of failure since it’s the only place where tokens can be used to look up the corresponding data. If the vault is overloaded or goes down, then performance and availability can suffer.

Integrating Data Tokenization with Other Controls

Tokenization is one element of an effective data security strategy and should be applied alongside other methods, such as encryption or masking. In general, tokenization is a good fit if there are systems that don’t require access to sensitive data but need some type of unique identifier in its place, potentially in a particular format.

In contrast, encryption provides a reversible way to protect sensitive data from exposure, making it a good fit for protecting data in transit or stored in a database. This complements tokenization by offering an option to secure systems where access to real data is needed.

Decision Matrix for Tokenization vs Other Controls

Data Tokenization Recap

Data tokenization protects sensitive data from exposure by replacing it with non-sensitive tokens where possible. By removing sensitive data from systems that don’t need access to it, tokenization reduces the risk of data breaches and the scope of regulatory compliance requirements.

The Cato SASE Cloud Platform offers a simple solution for implementing data loss prevention (DLP) with tokenization-supporting controls integrated into a global PoP network. By combining tokenization with other data protection techniques, Cato offers the tools companies need to meet security and compliance requirements. Explore Cato’s DLP and data protection solutions.

FAQ

What is the difference between data tokenization and encryption?

Data tokenization replaces sensitive data with non-sensitive tokens while maintaining a lookup table within a secure environment. Encryption performs mathematical transformations on data that can only be reversed with access to the correct decryption key.

Does data tokenization reduce PCI DSS or GDPR audit scope?

Tokenization can reduce PCI DSS and GDPR audit scope by decreasing the set of systems with access to sensitive data. With tokenization, only those systems with access to the real data – not the tokens -are subject to compliance requirements and audits.

What happens if a token vault is breached?

A token vault maintains a complete mapping between tokens and the real data that they represent. If breached, this exposes sensitive data and allows an attacker to associate tokens with real identities in data taken from other systems. For this reason, token vaults are a single point of failure and should be secured with encryption, access controls, and similar security best practices.

Can tokenized data be used for analytics?

Tokenization replaces sensitive data with a unique token, making it possible to perform analytics on the tokenized data. However, some information is lost as a result of tokenization, limiting the useful data that can be collected. For example, a company may know how many units of a product were purchased but not be able to break this down by location, etc.

Is data tokenization enough on its own?

Data tokenization is one aspect of a data security program and doesn’t replace encryption, DLP, and access control. Tokenization only reduces the set of systems that have access to the true, sensitive data; it doesn’t provide any way to protect data on systems that do need access to this data. For example, billing and shipping systems need access to real addresses, not tokenized ones.

Is vaultless tokenization safer than using a token vault?

No. Vaultless tokenization uses a mathematical formula to convert data into its tokenized form. This approach relies on the secrecy of the algorithm used to accomplish this. If an attacker guesses or learns the algorithm, then all tokenized data is exposed.

How does data tokenization impact system performance?

Tokenization has minimal performance impacts on systems using tokenized data, especially if format-preserving tokenization is used. However, operations that require data to be tokenized or for the real data to be retrieved from the token vault are slower because they require communication with the tokenization system, which is a centralized, single point of failure. These performance impacts may be lower than with encryption, and tokenization should be benchmarked under load, especially in cloud environments.