Anonos Prompt Protector: Secure and Private Generative AI & LLMs

Protect Data and Preserve the Effectiveness of Generative AI Tools

The Anonos Prompt Protector identifies and protects data within prompts, ensuring no sensitive information is transmitted to or from the generative AI tool.

With our patented relinking technology, the integrity and utility of the output remain intact, allowing for the protection of data and the preservation of the effectiveness of generative AI & LLMs.

Automatic PII Detection

The PII detection mechanisms identify and treat sensitive data such as names, email addresses, dates, locations, and even URLs with maximum security.

Robust Protection & High Utility

Unlike data masking and tokenization, Anonos offers robust protection without compromising data utility.

Embedded Data Protection

Safeguard enterprise data in untrusted environments with privacy controls that accompany the data wherever it travels.

Compatible with Major Data Ecosystems

Test, develop, and deploy AI-driven applications, while ensuring effortless integration with AWS, Databricks, Snowflake, and more.

Compliance & Reputational Security

Comply with data privacy regulations and emerging AI regulations. Ensure strong, measurable, and clearly auditable data protection.

Prevent Unauthorized Re-identification

Protect personal data from re-identification through Mosaic Effect (combining different datasets).

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Discover How Anonos Enables Secure AI Development & Use LEARN MORE

Anonos has an innovative and patented Variant Twin technology solution, proactively securing data wherever it travels, which is essential for controlling data privacy risk in cloud migration and data sharing projects.
Stewart Bond, Research VP

Anonos is cool because its patented technology creates re-linkable non-identifying personalized data called Variant Twins that enable compliant analytics, machine learning, AI and data sharing.
Gartner Cool Vendor Award
Gartner

Anonos helps joint customers accelerate their outcomes with data in a safe, effective and defensible way via methods such as pseudonymization of data and synthetic datasets for use cases like artificial intelligence and machine learning analytics.
Paige Bartley

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