Artificial Intelligence-Driven Data Integrity Verification in Cloud Computing
Keywords:
Artificial Intelligence, Data Integrity, Cloud Computing, Machine Learning, Anomaly Detection, Predictive Modeling.Abstract
In the evolving landscape of cloud computing, ensuring data integrity has become a critical challenge due to the increasing volume and complexity of data, as well as the diverse nature of cloud environments. Traditional methods of data integrity verification often fall short in addressing the dynamic and distributed nature of cloud architectures. This paper proposes an innovative approach to data integrity verification using Artificial Intelligence (AI) techniques tailored to the cloud computing paradigm. We introduce a framework that leverages machine learning algorithms to monitor, verify, and maintain data integrity in real-time across cloud environments. Our approach employs anomaly detection, predictive modeling, and automated error correction mechanisms to identify and rectify data integrity issues proactively. We conduct a series of experiments to evaluate the performance of the proposed framework, comparing it against conventional data integrity verification methods. Results demonstrate that the AI-driven approach significantly enhances the accuracy and efficiency of data integrity verification, providing more robust protection against data corruption and unauthorized modifications. The proposed framework shows promise in addressing the limitations of existing methods and adapting to the complexities of modern cloud computing environments.