Self-Optimizing AI Agents for Real-Time Security Enforcement in Dynamic Broadband Infrastructures

This paper explores a novel framework for deploying self-optimizing AI agents designed to enforce real-time security policies across dynamic broadband infrastructures.Given the rise of zero-touch networks, increasing traffic heterogeneity, and growing cyber threats, conventional reactive security methods are no longer sufficient.We propose an architecture that combines reinforcement learning (RL), federated observability, and edge-native threat detection.The paper introduces a scalable agent-based model with proactive anomaly detection and self-adjustment capabilities.Key contributions include a hybrid decision loop, a riskweighted policy optimizer, and an adaptive trust index.The proposed solution is validated through simulations and real-world telecom KPIs.The results demonstrate enhanced mean time to detect (MTTD), reduced false positives, and improved threat response efficiency.