Multi-Agent Firewall Architecture for Privacy Protection of Sensitive Data in Interactions with Language Models
Open-source multi-agent firewall intercepts sensitive data before it reaches external LLMs—a practical architecture for enterprises needing GDPR-compliant generative-AI integration.
Summary written by editorial AI · Source link below
arXiv:2607.08282v1 Announce Type: new Abstract: While Large Language Models (LLMs) have become essential productivity tools, their integration into workflows without adequate safeguards creates significant risks. This paper proposes an open-source, privacy-focused, user-facing firewall designed to secure both web-based and programmatic LLM interactions. The architecture combines a browser extension and a proxy for total traffic interception across both HTTP(S) and WebSocket communications. At i
Editorial Analysis
As enterprises adopt LLMs for productivity, uncontrolled data flows to external models create GDPR exposure; a privacy firewall reduces that risk at the architectural level.
Evaluate this multi-agent firewall approach for your organisation's LLM access layer and compare it against existing DLP controls.
An open-source privacy firewall for LLM interactions can reduce regulatory data-leakage risk as generative AI adoption accelerates.
Forward-looking interpretation drafted by editorial AI under human review — not a reproduction of the source. See methodology.
External link — opens at arXiv Crypto & Security in a new tab.
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