Introduction

Data analysis with privacy guarantees

Sarus’s mission is to unlock analytics, AI and GenAI on sensitive data without privacy risk. It is achieved by building a privacy layer between the sensitive data and the data practitioners: the data scientists can query the data any way they want, all the results that are returned are made privacy-safe by the application. The data owner first selects data sources that they want to make available for analytics or AI work and sets the privacy policies governing the types of outputs that are allowed to be retrieved. Then for each data processing task, Sarus will check it against the privacy policy, rewrite it into a compliant variant if necessary, execute it, and send results back to the data practitioner. The data was never copied or made available; all data processing tasks are logged.

High level architecture

Using Sarus typically involves three environments: Data sources where data is stored The Sarus Application having access to data sources and exposing an API for data practitioners A SDK and a BI connector for practitioners to use the API from their analysis environment (python or SQL)