Abstract

In the digital era, cloud computing has become integral to modern data storage and processing, offering scalability and cost-effectiveness. However, ensuring data integrity—defined as the accuracy, consistency, and reliability of data—remains a critical challenge. Breaches or corruption can lead to severe operational, financial, and reputational damage.

This research explores the application of Artificial Intelligence (AI) to strengthen data integrity in cloud environments. Leveraging machine learning for anomaly detection, deep learning for pattern recognition, and AI-based automation for real-time monitoring, the study proposes a robust framework to address data integrity threats. It examines prevalent issues like unauthorized access and data tampering, highlighting the limitations of traditional methods such as cryptography and manual audits.

By integrating AI into cloud infrastructure, this research emphasizes a proactive approach to anticipating and mitigating threats. Through case studies and experimental results, the study demonstrates the potential of AI-driven solutions to enhance trust and reliability in cloud computing, paving the way for future innovations in this critical domain.