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The Challenge of Running LLM on Sensitive Data
As organizations adopt Large Language Models for enterprise AI, they face a difficult tradeoff between maintaining expensive on-premises infrastructure and sending sensitive data into external cloud environments.
This demonstration shows how Duality enables secure LLM inference using Trusted Execution Environments (TEEs), allowing organizations to run modern AI workloads on sensitive and regulated data while maintaining privacy, governance, and control.
Confidential Computing creates hardware-isolated environments where prompts, documents, retrieval results, and model interactions remain protected during processing, including from the underlying cloud provider.
The demonstration covers secure LLM inference, confidential AI processing, Retrieval-Augmented Generation applications, remote attestation, zero-trust deployment workflows, governance and auditing controls, and hybrid and multi-cloud AI architectures.
Duality helps organizations protect sensitive prompts and enterprise data while giving teams the flexibility to deploy AI across cloud and distributed environments.
Whether building AI assistants, enterprise search, secure RAG applications, or regulated AI workflows, organizations can use modern LLM infrastructure while maintaining stronger control over sensitive data, model interactions, and deployment policies.