CloakLLM: PII Cloaking & Audit Logs for LLM API Calls

CloakLLM: PII Cloaking & Audit Logs for LLM API Calls

CloakLLM is an open-source tool designed to address critical security and privacy concerns when integrating Large Language Models (LLMs) into applications. It provides robust mechanisms for masking Personally Identifiable Information (PII) within LLM API calls and ensures tamper-evident logging of these interactions. This is essential for developers building AI-powered systems that handle sensitive data, ensuring compliance and protecting user privacy.

What it Does

CloakLLM intercepts LLM API calls, analyzes the data for PII, and applies masking techniques before the data is sent to the LLM provider. This prevents sensitive information from being exposed in logs or processed by external models. Simultaneously, it generates secure, immutable audit logs of all API interactions, providing a verifiable record of data flow and access.

Key Features

• PII Detection and Masking: Automatically identifies and masks various types of PII, such as names, addresses, phone numbers, and email addresses.
• Tamper-Evident Logging: Creates secure audit logs that are resistant to modification, ensuring data integrity and traceability.
• Customizable Rules: Allows developers to define specific rules for PII detection and masking based on their application's needs.
• Open-Source: Provides transparency and flexibility for integration and customization.
• LLM API Agnostic: Designed to work with various LLM providers and APIs.

Who it's For

CloakLLM is a vital tool for AI developers, data engineers, and security professionals working with LLMs. It is particularly relevant for those building applications in regulated industries like healthcare, finance, and legal services, where data privacy and compliance are paramount. Developers concerned with data leakage, unauthorized access, or maintaining auditable records of LLM interactions will find CloakLLM indispensable.