Symbiotect Network: A Federated Artificial Immune System for Advanced AI

Abstract

This concept paper proposes a Federated Artificial Immune System for advanced AI (FAIIS), an architecture for detecting, containing, learning from and sharing defenses against unauthorized AI behavior after a model begins to cross its intended boundary. The design borrows its structure from the human immune system. Sandboxes and permissions act as barriers. Local "Guardian" systems monitor behavior and can immediately take reversible protective actions, such as cutting network access or revoking credentials, without waiting for human approval. Confirmed incidents are turned into portable defensive "antibodies," which other independent Guardians test before those defenses gain wider authority. The system is federated rather than centralized: organizations keep their own data, share only abstracted defensive knowledge, and leave humans in control of irreversible actions. The paper is built to be falsifiable. It names specific conditions under which the idea should be rejected or revised and proposes a staged experimental program that can be run with current models. This is a systems-level concept paper, not a peer-reviewed technical result. It integrates existing work in AI control, cybersecurity, artificial immune systems, federated learning and threat-intelligence exchange into a testable proposal. AI language models assisted with drafting and revision; the concept and design choices are the author's.