Persistent data masking replaces sensitive data with similar-looking proxy data, which are typically randomly generated characters that will meet the requirements of a system designed to test or process the masked results.ĭata masking ensures vital parts of personally identifiable information (PII) and other confidential data, like the first five digits of a social security number, are obscured or otherwise de-identified.

Traditional key management enables encrypted data to be transformed back into cleartext (readable) for use in applications when needed, but it’s not a nuanced data privacy solution-it operates on files or data volumes, such as what you’d find in data archives.ĭata masking is often considered data-centric security, as it persists with the data when moved and used by hiding data elements that users of certain applications should not see. Fundamental differences between data masking and data encryptionįundamental difference: Encryption is typically applied to data at rest or data links (data in motion) where usability is not needed in a real-time application, such as long-term data storage or data transfers. Each of them is designed to help ensure data protection, which can be improved when both are used in synergy. There are many similarities between data masking and data encryption for data privacy solutions, although the differences are substantial. However, both can be useful to address regulatory compliance, such as the GDPR and CCPA and other data privacy use cases, such as protecting big data analytics to reduce data exposure risks. Data encryption, at the structured data field level, is a data masking function. Data masking and data encryption are two technically distinct data privacy solutions. Master Data Management & 360-Degree Views of the BusinessĪpplication Integration & HyperautomationĪ common misconception among data governance and data privacy stewards when considering data anonymization solutions is that encryption for data security is a form of data masking.

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