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Data masking is based on the premise that sensitive data can be transformed into less sensitive but still useful data. This is necessary to satisfy application testing use cases that require representative and coherent data, as well as analytics that involve the use of aggregate data for scoring, model building and statistical reporting. The market for data protection, DM included, continues to evolve with technologies designed to redact, anonymize, pseudonymize, or in some way deidentify data in order to protect it against confidentiality or privacy risk.

• Tonic
  5.0

  1 Review

Elevating Test Data Management for DevOps is the process of providing DevOps teams with test data to evaluate the performance, and functionality of applications. This process typically includes copying production data, anonymization or masking, and, sometimes, virtualization. In some cases, specialized techniques, such as synthetic data generation, are appropriate. With that, it applies data masking techniques to protect sensitive data, including PII, PHI, PCI, and other corporate confidential information, from fraud and unauthorized access while preserving contextual meaning.

• Tonic
  4.4

  37 Reviews