Disaster Recovery Strategies for Cloud-Based Insurance Platforms: Building Resilience and Ensuring Data Security (Journal of Computational Intelligence and Robotics, 2022)
The proliferation of cloud-based platforms in the insurance industry has introduced transformative efficiencies in operations, scalability, and customer service. However, these platforms are increasingly exposed to risks from natural disasters, cyberattacks, and system failures, necessitating robust disaster recovery strategies to ensure operational continuity and data security. This research paper explores advanced disaster recovery strategies specifically tailored for cloud-based insurance platforms, emphasizing cross-region disaster recovery planning, backup encryption mechanisms, and the importance of regular failover testing. The study investigates the critical challenges faced by cloud-based insurance systems, including regulatory compliance, data integrity, latency concerns during failover, and resource allocation for disaster recovery (DR) initiatives.The cornerstone of disaster recovery in cloud environments lies in cross-region replication and failover strategies, which enable insurers to minimize downtime and maintain service availability during regional outages. This paper provides an in-depth analysis of cross-region disaster recovery models, including active-active and active-passive configurations, while discussing their implications on latency, cost, and operational complexity. Backup encryption is another pivotal component in safeguarding sensitive insurance data from unauthorized access during disaster recovery operations. This research evaluates state-of-the-art encryption protocols, such as Advanced Encryption Standard (AES) and homomorphic encryption, examining their applicability and effectiveness in meeting industry-specific compliance standards such as GDPR, HIPAA, and PCI DSS.Regular failover testing is crucial in validating the reliability of disaster recovery plans. The paper outlines best practices for conducting failover simulations in a production-like environment to identify potential vulnerabilities and ensure that recovery time objectives (RTOs) and recovery point objectives (RPOs) are met. It also delves into the technical challenges and organizational resistance that often hinder the implementation of such tests, offering practical solutions to overcome these barriers.Additionally, the study highlights the integration of automation and artificial intelligence (AI) in enhancing disaster recovery processes. Automation tools, such as Infrastructure as Code (IaC) frameworks, enable the rapid provisioning and scaling of recovery environments, while AI-driven anomaly detection systems enhance the predictability of disaster events and optimize resource allocation. The economic implications of implementing comprehensive disaster recovery strategies are also examined, with a focus on balancing cost-effectiveness and operational resilience.Through detailed case studies of leading cloud-based insurance platforms, the paper demonstrates the real-world application and efficacy of these strategies. It examines how these organizations have leveraged cloud-native disaster recovery solutions to achieve near-zero downtime and stringent data security, even in the face of catastrophic events. Furthermore, the paper addresses the evolving landscape of cybersecurity threats and regulatory demands, emphasizing the need for a dynamic and adaptive approach to disaster recovery in the insurance sector.