ICETETI-2024 | View PDF
Paper Id:ICETETI-MSM126
doi: 10.71141/30485037/ICETETI-MSM126

Optimizing Disaster Recovery Plans Using RPA and AI-Driven Solutions for Cloud-Based Backup Systems

Oliver Wilson, Muhammadu Sathik Raja Sathik Raja M.S

Citation:
Oliver Wilson, Muhammadu Sathik Raja Sathik Raja M.S, "Optimizing Disaster Recovery Plans Using RPA and AI-Driven Solutions for Cloud-Based Backup Systems", International Journal of Multidisciplinary on Science and Management, pp. 203-208, 2024.

Abstract:
Disaster recovery (DR) planning is a critical component of business continuity management (BCM), ensuring that organizations can swiftly recover from system failures, cyber-attacks, and natural disasters. Traditional DR strategies often rely on manual intervention, which is time-consuming, prone to errors, and inefficient. With the advent of Robotic Process Automation (RPA) and Artificial Intelligence (AI)-driven solutions, organizations can significantly enhance their DR plans by automating backup processes, real-time threat detection, and predictive failure analysis. This paper explores the integration of RPA and AI in optimizing DR strategies for cloud-based backup systems. It provides a comprehensive analysis of existing literature, discusses innovative methodologies, and presents a case study illustrating the benefits of AI-driven DR automation. Additionally, it highlights key performance metrics, including Recovery Time Objective (RTO) and Recovery Point Objective (RPO), and demonstrates how automation improves these metrics. The findings indicate that leveraging AI and RPA minimizes downtime, reduces data loss, and enhances operational resilience. The study concludes by outlining best practices and future research directions for AI-driven DR planning.

Keywords: Disaster Recovery, Robotic Process Automation, Artificial Intelligence, Cloud Backup, Business Continuity, Recovery Time Objective, Recovery Point Objective.

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