ZeroTrusted.ai

Encrypts user prompts before they are sent to LLM providers, ensuring that the AI service provider cannot read the raw query content. Decrypts responses on the client side so users can read answers without the provider seeing plaintext interactions.

Automatically removes or masks personally identifiable information (PII) and identifying details from prompts before they reach the LLM. This prevents sensitive data from being exposed to model training pipelines or stored in provider logs.

Implements a zero-trust security model where no LLM provider, network intermediary, or external party is inherently trusted with user data. All data is encrypted and anonymized by default, with verification at each step of the interaction.

Receives encrypted responses from LLM providers and decrypts them client-side, ensuring that response data is never exposed in plaintext to intermediaries or stored unencrypted by the provider.

Provides connectors and middleware to integrate with popular LLM platforms (OpenAI, Claude, etc.) while maintaining encryption and anonymity layers. Acts as a privacy proxy between user applications and LLM APIs.

Records and logs all LLM interactions in an encrypted, tamper-proof manner, allowing organizations to maintain compliance audit trails without exposing the actual content of queries and responses.

Provides a free tier that allows individuals and small teams to test zero-trust LLM security features without financial commitment, enabling evaluation of privacy capabilities before enterprise deployment.