Gartner synthetic data generation vs masking report
Data masking tools vs synthetic data generation
Looking for effective data masking tools? The Gartner synthetic data generation vs masking report compares the pros and cons of synthetic data generation versus data masking
Discover how each approach protects sensitive information and ensures GDPR, CPRA, and HIPAA compliance, and learn which is best for you.
Fill this out and we'll email you the report
Gartner synthetic data generation vs masking 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.
