Data masking (or data anonymization) is increasingly used to protect sensitive data (e.g., personally identifiable information or PII) from unauthorized disclosure risks and to comply with strict privacy regulations (e.g., GDPR, HIPAA, CCPA). In this easy-to-read guide, you’ll learn the essentials of data masking including:
- The common use cases of data masking, such as test data management, analytics and BI, third-party vendor access, business continuity testing and more
- The common types of data masking, such as rules-based substitution, tokenization, masking out, and redaction
- The technology options for data masking and a comparison of their capabilities
- Static vs. dynamic data masking and the role of database virtualization and synthetic data
- Key considerations for data masking solutions
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