Edge AI for Autonomous Device Security Management

Abstract

For industrial, healthcare, consumer and enterprise environments, the number of connected devices has fundamentally expanded the attack surface of the modern enterprise beyond the reach of centralized security architectures. Resource constrained endpoints are generally connected intermittently via diverse communication protocols․ What is needed is not simply another security model: it need autonomous real-time enforcement solutions that do not rely on centralized infrastructure․ Edge Artificial Intelligence enables solutions to the structural imbalance by incorporating clever threat detection and response directly into the distributed device ecosystem․ The hierarchical deployment model supports on-device inference, gateway-level data aggregation, and federated learning, thus preserving data privacy while minimizing the communication burden over heterogeneous deployment environments․ Flexible machine learning models, including decision tree classifiers, temporal sequence models, and compressed neural network architectures, support accurate low-latency threat detection on devices with limited resources․ The system implementation comprises four layers: threat detection, access control, vulnerability management and automated incident response, forming a closed-loop enforcement system, which does not rely on a central controller․ The challenges posed by adversarial machine learning, model drift, regulatory compliance with law, and model explainability are addressed using adversarial training, federated learning, and interpretable model explanation frameworks․ The security architecture is a self-adaptive edge AI fabric, which is suitable for the perpetually-evolving distributed environment․