Which industry-standard data masking technique is recommended for credit card processing, where a token represents sensitive data records such as a credit card number?

Tokenization is the recommended industry-standard data masking technique for credit card processing because it replaces sensitive data, such as credit card numbers, with a unique identifier or token. This token has no usable value outside of a specific context, meaning that the actual credit card information is securely stored in a separate, protected environment.

This approach adheres to payment card industry standards, which prioritize the security of cardholder data. By utilizing tokenization, organizations can process transactions without ever exposing the original credit card numbers, significantly reducing the risk of data breaches and fraud. Even if the token is intercepted or accessed by unauthorized individuals, it holds no essential value and thus upholds the confidentiality and integrity of sensitive information.

In contrast, scrubbing involves removing or altering sensitive data from a dataset but may not provide the same level of security as tokenization for transaction processing. Anonymization attempts to de-identify sensitive data, but it can be less practical in financial transactions where the original data may still be necessary for certain operations. Integrity management focuses on ensuring the accuracy and consistency of data but does not inherently provide a mechanism for data masking or protection in payment processing contexts.