SafeLiShare ConfidentialAI™ Clean Rooms herald a new era of multiparty data collaboration, fostering advancements in research. Designed for the real-world AI application ecosystem, these clean rooms provide zero-trust, data privacy-compliant, and secure environments for collaboration without the need to share sensitive data or algorithms. Users can seamlessly merge sensitive inputs to unveil innovative insights at cloud speeds, streamlining processes for both data custodians and analysts.

Traditionally, collaboration involved tradeoffs:

• Some environments placed trust in a central party controlling all data and queries, relying on their commitment to keep inputs secure.
• Others offered limited matching capabilities, restricting the depth and breadth of insights.
• Still, others employed complex methods to safeguard data sensitivity, sacrificing analytical flexibility.

Yet, the risk of unauthorized access, data breaches, and inadequate security measures remains a concern, especially in fields like healthcare, where patient information confidentiality is paramount.

SafeLiShare ConfidentialAI™ Clean Rooms offer a simple solution, establishing secure multiparty computation focused on developing, implementing, and communicating ML models and large language models (LLMs) while ensuring privacy protection for training data and model parameters. This software addresses data security, intellectual property rights, and regulatory compliance concerns in AI-driven applications.

Industries such as healthcare, finance, and government, prioritizing data privacy, can safeguard their intellectual property and data confidentiality. By leveraging multiparty attestation and secure computation, they ensure the integrity of their datasets during training and inference processes, achieving high levels of accuracy and performance with AI models.