Enterprises are under growing pressure to innovate with AI while maintaining strict control over sensitive data. Analytics teams need broader datasets to improve model accuracy. Data science teams need realistic, multi-entity training data. Security and compliance teams need assurance that personally identifiable information (PII) and protected health information (PHI) are not exposed in development or experimentation.

This is where synthetic data generation tools play a critical role.

By generating realistic, statistically valid datasets that mirror production systems without exposing real sensitive values synthetic data enables secure analytics, faster AI experimentation, and safer collaboration across environments. But not all tools are built for enterprise complexity.

Below are ten synthetic data generation tools supporting secure enterprise analytics and AI in 2026, starting with platforms designed for enterprise-scale lifecycle management.

1. K2view

K2view provides enterprise-grade synthetic data generation tools designed to support secure analytics, AI model training, and software testing across complex, heterogeneous environments. Unlike model-only generators, K2view manages the entire synthetic data lifecycle from source extraction and masking to generation, operational controls, and CI/CD delivery.

K2view supports a multi-method approach:

• AI-powered generation for production-like realism
  
• Rules-based generation for controlled edge cases and new functionality
  
• Data cloning for large-scale load and performance testing
  
• Intelligent masking for compliance-driven lower environments
  

A key differentiator is its architecture, which preserves referential integrity across business entities such as customers, accounts, orders, and products. When generating synthetic datasets for analytics or AI training, relationships across systems remain intact, ensuring models behave realistically in production scenarios.

K2view also includes built-in masking and automated PII discovery, so production subsets used for model training can be anonymized before generation. Lifecycle controls including reservation, versioning, aging, and rollback allow teams to operationalize synthetic data delivery within CI/CD and MLOps pipelines.

Best suited for large enterprises with complex, multi-source environments, K2view offers a lifecycle-managed foundation for secure AI and analytics at scale.

2. Mostly AI

Mostly AI generates privacy-safe synthetic datasets that mirror real data distributions while protecting sensitive information. It focuses primarily on tabular and multi-relational data and offers fidelity metrics to compare synthetic output with source datasets.

For enterprise analytics, Mostly AI helps data science teams expand training coverage without directly exposing production data. Its user interface supports relatively fast dataset creation, making it accessible to teams with established data science workflows.

While strong in statistical fidelity and usability, organizations managing highly complex cross-system relationships may need additional governance or lifecycle tooling alongside the platform.

3. YData Fabric

YData Fabric combines data profiling with synthetic data generation, supporting tabular, relational, and time-series data. Its platform integrates into machine learning pipelines and includes automated data quality assessment.

For AI-driven analytics, YData can generate alternative market conditions, seasonal variations, and balanced datasets to improve model performance. It is particularly useful for firms developing ML models across multiple domains.

However, it requires data science expertise and may need additional configuration to fully align with all enterprise compliance requirements.

4. Gretel Workflows

Gretel offers a developer-focused synthetic data generation platform that integrates directly into pipelines. Supporting structured and unstructured data, it emphasizes automation and workflow orchestration.

For AI teams embedding synthetic generation into CI/CD or MLOps processes, Gretel enables scheduled dataset refreshes and API-driven workflows. It is particularly attractive to engineering-led teams.

The platform relies heavily on cloud infrastructure and is primarily developer-oriented, which may require complementary governance tools for broader enterprise adoption.

5. Hazy (SAS Data Maker)

Hazy, now part of SAS Data Maker, focuses on privacy-preserving synthetic data generation, using differential privacy and anonymization techniques.

In regulated industries such as financial services and healthcare, Hazy supports compliance-aligned synthetic data for analytics and AI. It preserves relational structures while ensuring strict privacy controls.

Setup can be complex, and the platform is generally best suited to larger enterprises where regulatory requirements justify the investment.

6. SDV (Synthetic Data Vault)

SDV is an open-source Python library supporting tabular, relational, and time-series synthetic data generation through models such as CTGAN and CopulaGAN.

For research teams and smaller data science groups, SDV offers flexibility and customization. It allows experimentation with generative models and relational constraints.

However, SDV lacks enterprise lifecycle management, governance controls, and integrated compliance capabilities, making it more suitable for technical users than as a centralized enterprise platform.

7. GenRocket

GenRocket began as a synthetic test data solution and has expanded to support analytics and AI use cases. It uses design-driven data generation aligned with predefined schemas and business rules.

For enterprises needing high-volume, rule-based synthetic datasets such as simulating large transactional flows GenRocket can be effective. It integrates into pipelines for automated dataset provisioning.

Because its core strength lies in synthetic generation rather than full lifecycle governance, organizations often pair it with additional data management tools.

8. Syntho

Syntho provides a self-service synthetic data engine focused on statistical realism and privacy compliance. It aims to preserve statistical properties while removing direct identifiers.

For analytics and forecasting use cases, Syntho can generate datasets that reflect both typical and rare scenarios, helping AI models learn beyond limited historical records.

Teams must define distribution priorities carefully, and governance processes may need to be managed alongside the platform.

9. Tonic.ai

Tonic.ai blends data masking and synthetic data generation to support engineering and analytics workflows. It focuses on delivering production-like datasets without exposing sensitive information.

For analytics teams seeking realistic development datasets with configurable generation logic, Tonic.ai can expand coverage while maintaining privacy controls.

Organizations managing highly complex cross-system dependencies may require additional lifecycle or integrity-preserving controls depending on the scope of their data landscape.

10. DataGen

DataGen specializes in generating synthetic datasets at scale for AI training, particularly in domains requiring high-volume simulation. It focuses on creating diverse, high-quality data to accelerate model development.

While effective for specific AI training needs, it is generally narrower in scope compared to platforms that combine generation with masking, governance, and lifecycle management.

Conclusion

Secure enterprise analytics and AI demand more than realistic data. They require governance, repeatability, compliance alignment, and operational control.

Some synthetic data generation tools focus on statistical fidelity. Others emphasize developer workflows or differential privacy. Open-source options provide flexibility but limited lifecycle management.

For enterprises operating across complex, multi-system environments, the differentiator is often the ability to preserve referential integrity, integrate masking and compliance controls, and operationalize synthetic data delivery within DevOps and AI pipelines.

Among the tools listed, K2view stands out for combining multi-method synthetic data generation with built-in masking, cross-system integrity, and lifecycle management. By unifying preparation, generation, operation, and delivery within a governed platform, it enables organizations to accelerate analytics and AI initiatives without compromising security or control.

As synthetic data moves from experimentation to enterprise standard, choosing the right platform will directly influence how securely and effectively organizations scale their analytics and AI capabilities.