About Sarus

Provides a privacy-preserving analytics and AI platform that enables data scientists and analysts to query sensitive data without direct access, using differential privacy, synthetic data generation, and privacy-first query rewriting. This approach ensures compliance with data protection regulations, prevents breaches, and allows organizations to unlock the full value of their data while maintaining security and privacy.

```xml <problem> Organizations struggle to unlock the full potential of sensitive data for analytics and AI due to privacy regulations, security concerns, and the risk of data breaches. Traditional anonymization techniques like data masking and hashing are often insufficient to prevent re-identification, leading to compliance issues and limiting data utility. Sharing data across silos and with external partners further exacerbates these risks. </problem> <solution> Sarus provides a privacy layer that enables data scientists and analysts to query sensitive data without direct access, ensuring compliance and maximizing data value. The platform leverages techniques like differential privacy, privacy-preserving synthetic data generation, and privacy-first query rewriting to protect data while preserving its analytical utility. Sarus integrates with existing data stacks and cloud environments, allowing organizations to perform research, analytics, and AI safely. By providing provable privacy guarantees, Sarus allows organizations to collaborate on sensitive data, accelerate project timelines, and unlock new insights without compromising data security or regulatory compliance. </solution> <features> - Differential privacy implementation to ensure query results are provably safe - Privacy-preserving synthetic data generation using generative AI models trained with differential privacy - Privacy-first query rewriting to transform data processing jobs into privacy-safe versions on-the-fly - Integration with Azure Confidential Clean Rooms for secure multi-party data collaboration - SarusLLM, enabling the use and fine-tuning of Large Language Models (LLMs) with private data safely - Qrlew, an open-source library designed to rewrite SQL queries into privacy-safe queries - Support for fine-tuning LLMs with differential privacy in Databricks - Output-level controls to ensure privacy compliance for all query outputs - Integration with existing data tools via SQL, pandas, and sklearn </features> <target_audience> Sarus targets data security teams, data protection teams, and AI & analytics teams within organizations in industries such as finance, healthcare, marketing, and the public sector. </target_audience> ```

What does Sarus do?

Where is Sarus located?

Sarus is based in Paris, France.

When was Sarus founded?

Sarus was founded in 2019.

How much funding has Sarus raised?

Sarus has raised 2730000.

Location

Paris, France

Founded

2019

Funding

2730000

Employees

17 employees

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Sarus

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Executive Summary

sarus.tech 2K+

Crunchbase

Founded 2019 – Paris, France

Funding

Estimated Funding

$2M+

Team (15+)

No team information available.

Company Description

Problem

Organizations struggle to unlock the full potential of sensitive data for analytics and AI due to privacy regulations, security concerns, and the risk of data breaches. Traditional anonymization techniques like data masking and hashing are often insufficient to prevent re-identification, leading to compliance issues and limiting data utility. Sharing data across silos and with external partners further exacerbates these risks.

Solution

Sarus provides a privacy layer that enables data scientists and analysts to query sensitive data without direct access, ensuring compliance and maximizing data value. The platform leverages techniques like differential privacy, privacy-preserving synthetic data generation, and privacy-first query rewriting to protect data while preserving its analytical utility. Sarus integrates with existing data stacks and cloud environments, allowing organizations to perform research, analytics, and AI safely. By providing provable privacy guarantees, Sarus allows organizations to collaborate on sensitive data, accelerate project timelines, and unlock new insights without compromising data security or regulatory compliance.

Features

Differential privacy implementation to ensure query results are provably safe

Privacy-preserving synthetic data generation using generative AI models trained with differential privacy

Privacy-first query rewriting to transform data processing jobs into privacy-safe versions on-the-fly

Integration with Azure Confidential Clean Rooms for secure multi-party data collaboration

SarusLLM, enabling the use and fine-tuning of Large Language Models (LLMs) with private data safely

Qrlew, an open-source library designed to rewrite SQL queries into privacy-safe queries

Support for fine-tuning LLMs with differential privacy in Databricks

Output-level controls to ensure privacy compliance for all query outputs

Integration with existing data tools via SQL, pandas, and sklearn

Target Audience

Sarus targets data security teams, data protection teams, and AI & analytics teams within organizations in industries such as finance, healthcare, marketing, and the public sector.