Secure Edge Computing for IoT Systems: AI-Powered Strategies for Data Integrity and Privacy

Abstract

The proliferation of Internet of Things (IoT) devices has significantly enhanced connectivity and operational efficiency across various sectors. However, this rapid expansion raises critical concerns regarding data integrity and privacy, particularly in edge computing environments where data is processed closer to the source. This paper explores AI-powered strategies for securing edge computing in IoT systems, focusing on safeguarding data integrity and ensuring user privacy. We analyze the vulnerabilities inherent in edge computing architectures and examine how artificial intelligence can mitigate these risks through real-time anomaly detection, predictive analytics, and robust encryption techniques. By employing machine learning algorithms and advanced cryptographic methods, we propose a comprehensive framework that enhances the security posture of IoT systems while maintaining the necessary performance metrics. Our findings suggest that integrating AI into edge computing not only fortifies data protection mechanisms but also improves operational resilience against potential cyber threats. Ultimately, this study aims to contribute to the development of secure, efficient, and privacy-preserving IoT ecosystems that are essential for the future of connected devices.