International Journal of Science, Engineering and TechnologyAuthors: Farhana Yasmin
Abstract: The escalating frequency of cyberattacks, critical system failures, and natural disasters has underscored the strategic importance of robust Business Continuity and Disaster Recovery (BCDR) frameworks across enterprise ecosystems. Traditional BCDR mechanisms, primarily reactive and procedural in nature, often struggle to adapt to the scale, velocity, and complexity of modern digital operations. Their reliance on manual intervention and static recovery models limits predictive accuracy and operational agility, thereby increasing downtime and potential data loss. The emergence of Artificial Intelligence (AI) technologies offers a paradigm shift in continuity and recovery management, transforming these frameworks into intelligent, adaptive, and data-driven systems capable of autonomous decision-making. This review critically examines the transformative impact of AI on BCDR practices, with emphasis on how AI-driven models such as machine learning (ML), predictive analytics, deep learning, and intelligent automation enhance risk identification, incident prediction, and recovery orchestration. AI systems can analyze historical and real-time data to identify early indicators of disruption, enabling preemptive responses that minimize operational impact. Furthermore, through intelligent orchestration, AI facilitates dynamic resource allocation, automated failover management, and optimized recovery sequencing, resulting in significant reductions in Recovery Time Objectives (RTOs) and Recovery Point Objectives (RPOs). The review synthesizes insights from academic literature and industrial implementations, presenting empirical evidence that AI-integrated continuity frameworks outperform traditional models in resilience, cost-efficiency, and scalability. Beyond performance gains, AI introduces new dimensions to continuity strategy such as cognitive automation, self-learning resilience, and predictive maintenance which collectively enable continuous adaptation to evolving risk landscapes. However, the integration of AI into BCDR is not without challenges. Issues related to data integrity, model interpretability, and integration complexity remain significant barriers to adoption. Moreover, ethical and regulatory considerations must be addressed to ensure transparency and accountability in AI-driven decision-making during crisis scenarios.
