Leverage the power of confidential computing for secure collaboration
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
Secure model IP no matter where the computations must be run. Customize models on client data in their infrastructure and scale model monetization.
Support any data types: Structured and unstructured data (e.g. text, audio, images etc..)
Support any type of models and computation including feature engineering , popular ML framework and advanced models (i.e. LLM)
Improve AI/ML model performance by training, tuning and validating AI and ML models on encrypted data.
Optimize efficiency by working collaboratively across borders while meeting data privacy and residency requirements.
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.
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.
Maximize the value of sensitive, regulated, or confidential data.
