Data Masking vs Synthetic Data: Compare Privacy Methods

Compare data masking & synthetic data with Gartner’s Market Guide.

Data masking isn’t enough for testing, analytics, and AI. Maintaining data utility, consistency, and referential integrity is challenging, so teams compare masking techniques with synthetic data.

Compare privacy methods based on:

  • Data masking vs synthetic data 

  • Anonymization, tokenization, and redaction tradeoffs

  • Data utility vs privacy protection

  • Alternatives to data masking
Gartner Market Guide for Data Masking and Synthetic Data

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Key capabilities to compare in data masking software

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Data masking vs synthetic data

Compare masking, anonymization, tokenization, and synthetic data across real-world use cases.

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Masking limitations and consistency

See where masking impacts data utility, consistency, and referential integrity across systems.

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Data utility vs privacy tradeoffs

Evaluate how different methods affect realism, usability, and compliance for testing and AI.

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Privacy approaches and vendors

Explore tools, techniques, and providers to identify the right data privacy strategy.

Where data masking fits,
and where it falls short

Data masking is an effective way to protect sensitive data, but maintaining data utility, consistency, and referential integrity across systems can be challenging - especially for testing, analytics, and AI, and LLM applications. 

As a result, teams evaluate when masking is sufficient and when additional approaches, like synthetic data, are needed. 

  • Testing and development: Need realistic data
  • Analytics and AI:  Need high-utility data 
  • Consistency and referential integrity: Need consistent, relational data 
PII masking blended with synthetic data and privacy techniques

Who this guide is for

  • Infosec: Compare modern privacy and compliance methods 
  • Data engineering: Solve complex referential integrity issues  
  • Analytics & AI: Access high-fidelity, privacy-safe data  
  • Quality engineering: Leverage production-like data for testing 
Compliant data supporting infosec, data engineering, analytics and AI, and quality engineering teams
Gartner Market Guide for Data Masking and Synthetic Data

Compare data masking vs. alternatives with Gartner’s guide

Evaluate data masking, anonymization, and synthetic data using Gartner’s framework—so you can choose the right approach for your use case. 

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