Tool Overview:
Sarus

About Sarus

Sarus is a data privacy platform that specializes in enabling organizations to explore and utilize sensitive data while maintaining strict privacy controls. The platform combines differential privacy techniques with data exploration capabilities, allowing data practitioners to analyze and build AI applications without directly accessing raw data. Instead of traditional anonymization or data masking approaches, Sarus implements mathematical privacy guarantees through differential privacy.

What does Sarus do?

The platform provides a query-only interface where analysts and scientists can retrieve privacy-safe results without direct access to the underlying data. Through this interface, users can perform cohort analysis, run statistical computations, and develop AI applications while the platform automatically applies differential privacy guarantees to the outputs. Sarus includes synthetic data generation capabilities, allowing users to work with representative but non-sensitive versions of the data for development and testing purposes.

A key component of the platform is its clean room environment, where data processing occurs in a controlled space that ensures source data remains protected. The clean room automates the validation of processing workloads, eliminating the need for manual privacy risk assessment of each computation. This automation addresses traditional challenges in data clean rooms, where data owners must manually evaluate whether confidential information might leak into processing outputs.

For machine learning and AI applications, Sarus provides specific capabilities for privacy-safe LLM workflows. Data scientists can explore, preprocess, and feed data to language models without directly viewing sensitive information. The platform implements automated Differentially Private Stochastic Gradient Descent (DP-SGD) during model fine-tuning to ensure no personal data is embedded in the resulting models.

What makes Sarus different?

The platform's approach differs from traditional privacy tools by implementing a zero-trust mindset where data never moves from its secure infrastructure. Rather than relying on data masking or traditional anonymization, Sarus applies differential privacy techniques that provide mathematical guarantees of privacy protection regardless of an attacker's auxiliary information or computing power. This makes the protection future-proof, as it does not depend on current assumptions about what information might be available to adversaries.

Another factor is Sarus's ability to automate privacy-safe outputs at scale. Instead of requiring manual review of each processing task, the platform can validate workloads automatically while maintaining strong privacy guarantees. This enables organizations to run multiple iterations of analysis or machine learning model development without increasing privacy risks or administrative overhead.

Use cases and industries

Sarus addresses privacy challenges across multiple sectors, including finance, healthcare, marketing, and the public sector. Common applications include security compliance, where organizations need to protect sensitive data while maintaining analytical capabilities, and multi-site analysis scenarios where data from different locations needs to be analyzed without centralization.

The platform supports specific use cases such as privacy-safe LLM workflows, high-fidelity test data generation, and insider threat management. In financial services, it enables institutions to pool transaction data for fraud detection while maintaining strict privacy controls. For healthcare organizations, Sarus allows analysis of patient data while ensuring compliance with privacy regulations through its differential privacy guarantees.