Train and deploy sensitive models on sensitive data—across institutions or clouds—without compromising privacy, IP, or regulatory compliance.
Whether you’re working with partners, regulators, or internal teams, Duality enables you to collaborate on AI without risking data or revealing proprietary models. Run your workflows with partners securely—on any model, any data, anywhere.
Run federated or centralized analytics using a combination of PETs like FL, TEE, and DP—ensuring privacy, security, and compliance across any setup.
Maintain full compliance with data protection and AI governance requirements, while preserving the confidentiality of your data, models, and algorithms.
Work with multiple partners on joint AI projects while maintaining strict isolation, access control, and operational governance.
Run training or inference directly on regulated, proprietary, or confidential data—without exposing it to collaborators or cloud providers.
Duality Federated AI enables organizations to train and analyze AI models collaboratively—without moving sensitive data and while protecting local computations results. Built on the foundations of NVIDIA FLARE and all major TEE cloud vendors, this solution combines federated learning with trusted execution environments to deliver privacy, compliance, and performance at scale. The platform supports data preprocessing, role-based governance, and automated encryption, enabling organizations to securely collaborate across geographies, sectors, and regulatory boundaries.
Whether it’s fraud detection, medical research, or model trials, organizations can use sensitive, distributed datasets without sacrificing control, accuracy, or privacy.
Raw data never moves, and intermediate model updates are protected with secure enclaves.
Enable joint AI development without moving and exposing raw data—across hospitals, banks and insurers, government agencies or any distributed organizations.
Streamline federated AI deployment with automated encryption and attestation, built-in project management, participant roles, and policy enforcement.
Duality Centralized AI enables secure training and deployment of AI models in trusted cloud environments—without compromising the privacy of data or models. All assets, including data and models, are encrypted locally and transmitted securely to a Trusted Execution Environment (TEE), where they are decrypted and processed in isolation.
This architecture ensures that sensitive data remains protected throughout its lifecycle. Even the cloud provider, platform administrators, or infrastructure owners cannot access the decrypted data or model. Results are re-encrypted before they leave the TEE and delivered only to authorized recipients—guaranteeing both confidentiality and trust.
Assets are encrypted at the source, processed only inside secure enclaves, and returned as encrypted results.
Deploy the platform on-premises or in any major cloud provider, and support secure cross-cloud collaborations—all while maintaining full data confidentiality.
Streamline Centralized AI deployment with automated encryption and attestation, built-in project management, participant roles, and policy enforcement.
Supports all major frameworks and data formats for maximum flexibility.
Single platform for all use cases. Choose the right topology for your needs Federated or Centralized.
Run securely on AWS, Azure, GCP ,hybrid infrastructure or on-prem.
Keep data, models, and results protected—no party sees more than they should.
Safeguard proprietary algorithms and training data from external or internal leakage.
Meet privacy, AI governance, and compliance requirements out of the box.
Cancer research often requires large-scale analysis of digital pathology images spread across hospitals and research centers. Privacy regulations make direct data sharing difficult, especially when dealing with protected health information (PHI). With Duality Collaborative AI, Dana-Farber Cancer Institute was able to collaborate with partners to train a cancer classification model from decentralized pathology images. Data never moved, and only encrypted model updates were transmitted and aggregated securely using Trusted Execution Environment. The result: a high-quality model trained across institutions, matching the performance of centralized training—without breaching patient confidentiality.
Detecting financial fraud requires collaboration across banks and financial institutions—but sharing sensitive transaction data poses legal and competitive risks. With Duality Collaborative AI, financial institutions can securely train machine learning models on distributed transaction data without centralizing or exposing it. Each institution trains locally, with only encrypted model updates shared and aggregated inside secure enclaves. This enables cross-institution model development for fraud detection while protecting client privacy and proprietary algorithms.
Enable privacy-preserving data collaborations across your entire financial ecosystem.
Protect patient data across your healthcare network through privacy-preserving collaborations.
Transform your marketing strategies with data collaborations that respect privacy, driving digital advertising efforts with confidence and creativity.
