Data Layer Privacy
Subsalt’s Generative Database allows you to safely and easily provision privacy-preserving data for machine learning, business intelligence, advanced analytics, and research

Subsalt’s Generative Database allows you to safely and easily provision privacy-preserving data for machine learning, business intelligence, advanced analytics, and research
Subsalt’s Generative Database guarantees privacy protections and regulatory compliance for existing data science, machine learning, and data sharing workflows and applications
Synthetic data preserves statistical properties of sensitive data set, without exposing entities in the data
Data consumers receive data that has been proven to perform similarly to source data for their intended use case
Synthetic data is computer-generated data that mirrors the statistical properties and column structure of an underlying data set. It looks and feels like real data. It can be used to generate the same insights as your sensitive data. And because it's new, computer generated data, it guarantees privacy.
Synthetic data preserves statistical properties of source data and preserves privacy for entities in the data set. This ensures compliance with privacy laws and unlocks risk-free data analysis and data sharing without restrictions on data use, destructive redactions, or expensive legal and compliance processes.
Synthetic data isn't a viable replacement for real data in every case. For processes that require user or patient identity to be preserved, like support or clinical operations, real data is still required. But for data science and analytics, synthetic data is the best way to provision safe, private data.
Eliminate long, expensive legal and compliance reviews and custom data engineering required to provision sensitive data. By making compliant data available at the infrastructure layer, Subsalt enables fast, consistent, scalable data provisioning.
Subsalt provides access to synthetic data proven to perform similarly to the source data for each analysis. This ensures that Subsalt's infrastructure can serve as shared data infrastructure for many data consumers.
Subsalt can interact with your existing data stack, from BI tools and ML libraries to Jupyter Notebooks and governance tools. By delivering row-level data access through standard interfaces, Subsalt is designed to introduce a compliant data source without requiring changes to downstream tools and systems.
Synthetic data produced by Subsalt's system is provably privacy-preserving. By ensuring this protection at the data source, downstream analysis can be performed without risking compliance with privacy regulations or disclosure of private information in the event of a breach.