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ToolMesh

OneAIFW Introduces Local AI Firewall for Sensitive Data Protection

Emily Carter
Emily Carter
AI Data PrivacyOneAIFW
A digital shield with an AI brain icon, representing a local AI firewall protecting sensitive data from large language models.

As AI systems move beyond text generation and into more integrated applications, the handling of sensitive personal information (PII) within large language models (LLMs) presents a significant challenge. OneAIFW has introduced a local "AI firewall" designed to address this by de-identifying sensitive data before it reaches an LLM and restoring it upon response.

Key Points

OneAIFW operates as a local intermediary, ensuring that real PII never leaves a user's device when interacting with various LLMs. The system automatically identifies and replaces sensitive information with placeholders before data is sent to an AI model. Upon receiving a response, these placeholders are then reverted to the original sensitive data. This process is executed entirely on the user's local machine, preventing PII from being transmitted to cloud-based AI services or stored in their logs.

Under the Hood

The OneAIFW system is engineered to recognize a broad spectrum of sensitive data types. This includes, but is not limited to:

  • Real names

  • Detailed home and company addresses

  • Email addresses

  • Phone numbers (both domestic and international formats)

  • Bank card numbers and Alipay accounts

  • Passwords, verification codes, and login tokens

  • Cryptocurrency mnemonic phrases, private keys, and wallet addresses

  • ID card numbers and passport numbers

From a structural standpoint, the core of OneAIFW is written in Zig for performance. The project offers multiple deployment options, including a web version that runs entirely in the browser, a browser extension for seamless integration with popular LLM web interfaces (e.g., ChatGPT, Claude, Poe, Tongyi Qianwen), and a local proxy service. For developers, the proxy service allows users to redirect all LLM API calls through http://127.0.0.1:8844, enabling automatic de-identification across various models such as OpenAI, Anthropic, Groq, DeepSeek, and SiliconFlow. Docker deployment is also available for streamlined setup.

What Comes Next

The project's GitHub repository, located at https://github.com/funstory-ai/aifw, provides access to the source code and installation instructions. An online experience is also available at https://oneaifw.com, allowing users to test the functionality directly within their browser, completely offline. This local processing ensures that no cleartext sensitive information is exposed outside the user's computer at any point during the interaction.