The Duality Secure AI Collaboration Platform enables organizations to train and deploy AI and machine learning models on sensitive, external datasets without moving or exposing the underlying data. It applies privacy-enhancing technologies to allow multi-party collaboration on data that would otherwise be inaccessible due to privacy, security, or regulatory constraints. The platform supports a range of model types, from traditional machine learning models to neural networks and generative AI. It is designed to address use cases where data owners and model vendors need to collaborate without either party exposing their proprietary assets — data or algorithms. Key use cases include: - Government: Collaborative training of cybercrime, financial crime, and national security models across public and private sector partners. - Healthcare: Deploying AI models on medical imaging data linked with PII and PHI for pathology prediction; linking genomic data for health risk prediction and precision medicine. - Financial Services: Building risk scoring models by combining features across data vendors and financial institutions. - Model Evaluation: Testing third-party or generative AI models against real customer data before purchase decisions. The platform is available for on-premises deployment and on major cloud platforms. It provides governance and access controls to enforce privacy-protected collaboration. Vendors can use it to prove model value on real customer data earlier in the sales cycle while protecting their IP.