GARTNER® MARKET RESEARCH
Data Masking: Protecting Sensitive Data
Looking for effective data masking tools? This Gartner report explores the strengths and weaknesses of traditional data masking techniques and the rise of synthetic data generation as a powerful alternative.
Discover how each method protects sensitive information and ensures GDPR, CCPA, and HIPAA compliance, and learn which approach is best suited for your specific needs and use cases.
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Gartner Report Highlights:
- Data Masking and Synthetic Data: Discover when to leverage synthetic data for software testing and when to apply data masking techniques to protect PII.
- Data Protection Strategies: Learn how to safeguard sensitive data, evaluate synthetic data feasibility, and calculate ROI.
- Best Practices: Get recommendations on combining data protection methods, managing costs, and ensuring compliance.
Vital Market Research and Analysis
In closed systems or proprietary software where the underlying data models are inaccessible, synthetic data generation techniques may not be suitable due to the inability to mimic the system’s structural and statistical properties accurately. In such cases, masking techniques should be used to provision test data, ensuring data security while maintaining effective testing.
