Anonos: End-to-end data security for your LLM workflows.

Struggling to use and protect your enterprise data?

Traditional data protection methods fail to secure data in use within LLMs & RAG, exposing your business to:

Disclosure Risks: LLMs may reveal sensitive information from seemingly harmless inputs.
Cloud Hosting & Data Sovereignty: Concerns over sending data to third-party clouds, especially with data sovereignty laws.
Compliance Hurdles: Regulations like GDPR and HIPAA restrict the use of sensitive data, making it difficult to fully utilize AI models.

Expect generative Al to have the largest impact on their business out of all emerging technologies

Think generative Al implementation introduces moderate to high risk concerns

Source: KPMG, "Generative AI: From buzz to business value", 2023. An exclusive KPMG survey shows how top leaders approach generative AI.

End-to-end data security without compromising utility or compliance

Anonos' LLM protection solution safeguards sensitive unstructured data in LLM workflows, offering policy-driven granular protection and selective disclosure.

Unlike other data protection tools, Anonos safeguards sensitive information end-to-end, from data ingestion to output generation.

Real-World Applications

Anonos partners with some of the world’s largest enterprises to solve their sensitive data challenges, enabling the use of LLMs and RAG securely and compliantly.

FINANCIAL SERVICES

A major financial institution used Anonos' LLM protection to secure client data, enabling customer service agents to access needed information, reducing compliance risks, and saving millions in operational costs.

HEALTHCARE

Anonos' LLM protection enabled a healthcare provider to analyze patient data while remaining HIPAA compliant, improving patient outcomes without risking sensitive disclosures.

TECH ENTERPRISES

An engineering team used Anonos' LLM protection to safeguard proprietary information in technical documentation while providing access to essential design data.

How it Works

Detection and Classification: Identify and label all types of sensitive data in unstructured text.
Reversible De-identification: Reversibly tokenize sensitive data during processing to preserve utility and ensure protection.
Selective Disclosure: Technologically enforce access - only authorized users can view protected data.
Real-time Adaptations: Modify protection policies and data sensitivity levels in real-time without the need to retrain or reload models.
End-to-End Protection: Protect sensitive data throughout the LLM workflow - including during RAG ingestion and retrieval and LLM outputs.