Duality enables secure collaboration using confidential computing
With the new and emerging confidential computing (AKA trusted execution environment) customer can run a wide range of analytics and ML computation while collaborating on their sensitive data with their peers in a highly secure trusted execution environments. This paradigm shift enable a practical solution to the problem of protecting sensitive private data while being processed for advanced machine learning including LLM
Computation performance is similar to running in the clear
Built-in tools for aligning schemas, transforming data, and pre-processing for multiparty collaboration.
Hardware backed proofs of execution of confidentiality based on cloud trusted execution environment
Ensure full control on your data, decide who accesses the data, which computations will run, at what frequency, and more.
Support any data types: Structured and unstructured data (e.g. text, audio, images etc..)
ML & LLM
Support any type of models and computation including feature engineering , popular ML framework and advanced models (i.e. LLM)
Model Training, Testing and Validation
Improve AI/ML model performance by training, tuning and validating AI and ML models on encrypted data.
Cross-Border Data Analysis
Optimize efficiency by working collaboratively across borders while meeting data privacy and residency requirements.
Medical Multi-Center Studies
Analyze sensitive medical data across centers to gain deeper insights to speed research and discovery.
Access new high-value data sets in a privacy-protected and compliant manner.
to the Cloud
Protect sensitive data and models while transitioning AI workloads to the cloud.
Apply third-party analytics and ML models to your sensitive data without exposing it.
Deliver personalized customer experiences and upsell with partners while preserving privacy.
Create new revenue sources from sensitive data while preserving privacy and regulatory compliance.
Experience secure collaborative computing today.
Maximize the value of sensitive, regulated, or confidential data.