This guide defines data masking as the process of replacing sensitive data with structurally similar but fictitious values to support compliance, privacy, and security. It details regulatory drivers, common use cases (DevOps, analytics, vendor access, auditing), masking techniques (nullification, substitution, tokenization, redaction), and the difference between static and dynamic masking. The guide also compares masking technologies, discusses database virtualization and synthetic data, and provides evaluation criteria for selecting scalable, enterprise-ready masking solutions.

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