Toward Agentic AI Intrusion Detection and Self-Healing Security for UAV Network in Next-Generation Wireless Systems

Unmanned Aerial Vehicles (UAVs) are emerging as key enablers of next-generation wireless networks, supporting flexible connectivity, cooperative sensing, and edge intelligence in envisioned 6G systems. However, the open, highly dynamic, and resource-constrained nature of UAV networks introduces significant security challenges that are poorly addressed by conventional centralized or static intrusion detection mechanisms. This article explores an Agentic AI-driven intrusion detection and self-healing security architecture; featuring autonomous agents with local perception-decision-action loops tailored for UAV-enabled wireless networks. By embedding autonomous intelligence, trust-aware collaboration, and adaptive decision-making across sensing, detection, and response layers, the proposed architecture enables localized anomaly detection, proportional mitigation, and autonomous recovery without continuous centralized control, achieving energy efficiency through localized inference and reduced communication overhead. A simulation-driven case study evaluates metrics like detection latency and accuracy, illustrating how agentic behavior evolves across interaction cycles, demonstrating adaptive trust degradation, bounded collaboration, timely mitigation, and effective self-healing while preserving mission continuity. Comparative performance characteristics further highlight the advantages of agentic security over static rule-based and centralized AI approaches in terms of responsiveness, adaptability, and energy efficiency. Rather than focusing on algorithm-specific optimization, this work emphasizes architectural design principles and system-level behavior, offering practical insights toward resilient, explainable, and scalable security solutions for UAV-enabled 6G networks.