AI-Enabled Security and Data Integrity in Cloud and Edge Environments

Abstract

In the era of digital transformation, ensuring robust security and data integrity in cloud and edge computing environments has become paramount. The proliferation of interconnected devices and cloud services has intensified the need for advanced security solutions that can safeguard data against emerging threats while maintaining its integrity. This paper explores the role of Artificial Intelligence (AI) in enhancing security measures and ensuring data integrity across cloud and edge computing platforms. We propose an AI-enabled framework that leverages machine learning algorithms for real-time threat detection, anomaly detection, and predictive analytics to preemptively address potential security breaches and data corruption. Through a comprehensive review of recent advancements and methodologies, including the use of deep learning, reinforcement learning, and anomaly detection techniques, we evaluate their effectiveness in identifying vulnerabilities, mitigating risks, and maintaining data consistency. The proposed framework is assessed through a series of experiments and case studies, demonstrating its capability to adapt to various attack vectors and environmental changes. Our findings highlight the significant impact of AI in transforming traditional security paradigms and enhancing the resilience of cloud and edge systems against sophisticated threats. This paper provides a detailed analysis of the benefits, challenges, and future directions for AI-driven security solutions in modern computing environments, aiming to offer a foundational perspective for researchers and practitioners seeking to advance the state of security and data integrity