Secure Analytics
Teams can calculate statistics, identify patterns, compare populations, develop risk models, and perform analytical workflows across multiple data owners while each organization retains control of its source information.
Secure Analytics is designed for scenarios where the value comes from understanding patterns across datasets rather than retrieving individual records. Applications include financial crime analysis, healthcare and life sciences research, government analytics, risk modeling, operational intelligence, and multi-institution studies.
Duality combines privacy-enhancing technologies and distributed computation to protect sensitive information during analysis while supporting governance, access controls, and auditability.
The result is a way for organizations to answer questions that require broader datasets while maintaining the security, sovereignty, and governance requirements surrounding the original information.