Introduction
Data tokenization is a data security strategy that involves replacing sensitive data, such as credit card numbers or social security numbers, with randomly generated characters called tokens. The tokens have no inherent meaning and are not reversible, meaning they cannot be used to obtain the original data they represent. The only way to retrieve the original data is through a process called de-tokenization, which requires access to the system that created the token.
How Does Data Tokenization Work?
The process of data tokenization involves mapping sensitive data to tokens using methods that make the token impractical or impossible to restore without access to the tokenization system. In this process, the original data is replaced by a token, which has the same format as the original data but contains entirely random characters.
There are two main approaches to data tokenization:
- Vault-based Tokenization: In this approach, a token vault serves as a dictionary of sensitive data values and maps them to token values, which replace the original data values in a database or data store. The token vault is the only place where the original information can be mapped back to its associated token.
- Vault-less Tokenization: In the case of vault-less tokenization, tokens are stored using an algorithm instead of a secure database to protect private data. The original sensitive information is typically not kept in a vault if the token is reversible.
Importance of Data Tokenization
Data tokenization plays a crucial role in protecting sensitive data from cyber threats and data breaches. It helps organizations comply with data protection regulations such as the General Data Protection Regulation (GDPR) while minimizing the complexity and cost of compliance.
Data breaches can be costly, and according to an IBM report, data breaches now cost companies $4.24 million per incident on average. By adopting data tokenization, companies can minimize exposure of sensitive data in applications, stores, people, and processes.
Difference between Data Tokenization and Encryption
Data tokenization and encryption are two different approaches to data privacy. While both are data obfuscation techniques that help secure information in transit and at rest, they differ in several ways.
Encryption converts plaintext information into a non-readable form, called ciphertext, using an encryption algorithm and key. On the other hand, tokenization replaces data with a randomly generated token value that has no inherent meaning. While encryption is ideal for exchanging sensitive information with those who have an encryption key, tokenization is great for organizations that want to stay compliant and minimize their obligations under regulations like PCI DSS.
Use Cases of Data Tokenization
Data tokenization is used in a wide range of scenarios to safeguard sensitive data, including:
- PCI DSS Compliance
The Payment Card Industry Security Standard (PCI DSS) applies to any organization that accepts, processes, stores, or transmits credit card information. Tokenization is used to satisfy this standard because tokens are not typically subject to compliance requirements such as PCI DSS 3.2.1, provided there is sufficient separation of the tokenization implementation and the applications using the tokens.
- Third Party Data Sharing
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.
- Principle of Least Privilege Management
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.
Conclusion
Data tokenization is an effective data security strategy that can help organizations protect sensitive data from cyber threats and data breaches. It ensures compliance with data protection regulations while minimizing the complexity and cost of compliance. By adopting data tokenization, organizations can safeguard sensitive data and build trust with their customers by providing them with the peace of mind that comes with knowing their personally identifiable information will not fall into the wrong hands.
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