AI-Driven Security and Privacy Framework for Organizational IoT Operations

This research provides a multi-layered, artificial intelligence framework to enhance security and privacy within organizational Internet of Things (IoT) operations. While IoT proliferation offers automation benefits, it significantly expands the attack surface. Current centralized or blockchain-based models fail to simultaneously optimize detection accuracy, latency, and energy consumption. The methodology integrates edge-based anomaly detection for rapid response, federated learning for privacy-preserving model training, and cloud-level auditing for compliance. The system was validated using 550,000 data samples across healthcare, smart home, and vehicular scenarios. Results demonstrate 98% detection accuracy, 210 ms latency, and a low energy consumption of 2.5 W per node. The framework maintained stable performance with a low 0.9% privacy violation rate, outperforming traditional architectures in cross-domain stability. These findings indicate that artificial intelligence acts as a mediation tool to balance conflicting system objectives. This framework provides a scalable, context-aware paradigm for securing heterogeneous IoT ecosystems.