AI-Powered Disaster Recovery Planning and Failover Optimization for Mission-Critical Enterprise Systems

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@article{1718609,
      author = {Tahseen Zafar},
      title = {AI-Powered Disaster Recovery Planning and Failover Optimization for Mission-Critical Enterprise Systems},
      journal = {Iconic Research And Engineering Journals},
      year = {2026},
      volume = {9},
      number = {12},
      pages = {581-591},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1718609.pdf},
      abstract = {Mission-critical enterprise systems now operate through hybrid combinations of private data centres, public cloud zones, edge services, identity platforms, databases, application programming interfaces, and third-party digital supply chains. Conventional disaster recovery planning, while still necessary, is increasingly insufficient because static runbooks cannot anticipate fast-moving failures, cascading dependencies, ransomware disruptions, configuration drift, and volatile workload peaks. This review examines how artificial intelligence can strengthen disaster recovery planning and failover optimization through predictive failure analysis, anomaly detection, automated root cause reasoning, workload forecasting, policy-aware orchestration, and human-supervised remediation. A structured narrative review method was applied to recent research and standards published between 2020 and 2025, with emphasis on AIOps, cloud reliability, critical infrastructure protection, generative artificial intelligence, autoscaling, microservice observability, cyber resilience, and governance. The paper proposes an integrated AI-driven disaster recovery lifecycle that links telemetry ingestion, risk scoring, scenario modelling, failover decisioning, validation, and post-incident learning. The review indicates that the strongest value of AI lies not in replacing continuity professionals but in compressing detection-to-decision time, reducing alert noise, identifying probable blast radius, and recommending recovery actions aligned with recovery time objectives, recovery point objectives, security controls, and service-level commitments. However, the literature also reveals persistent limitations, including explainability gaps, poor data quality, adversarial model risk, over-automation, weak integration with legacy platforms, and uncertain accountability during autonomous failover. The study concludes that AI-powered disaster recovery should be designed as a governed socio-technical capability, combining machine intelligence, resilient architecture, audited automation, and expert approval for high-impact actions.},
      keywords = {Artificial Intelligence, Disaster Recovery Failover Optimization AIOps, Mission-Critical Systems, Cyber Resilience, Business Continuity.},
      month = {June},
      doi = {https://doi.org/10.64388/IREV9I12-1718609}
  }