GARTNER® MARKET RESEARCH: DATA MASKING & pseudonymization TOOLS
Data Masking and Synthetic Data: Protecting Sensitive Data
Obtaining high-quality test data while protecting sensitive information is a significant challenge in software development. Data masking and synthetic data generation offer robust approaches to safeguarding your data and ensuring compliance.
Download this April 2024 research by analyst firm Gartner to learn when it's best to employ data masking tools and when synthetic data generation is the optimal choice.
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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."
