Systematic framework for implementing effective disaster recovery plans in banking IT infrastructure

Posted: Jul 18, 2024

• Aria Anderson
• Aria Hill
• Ava Scott

Abstract

The banking sector's increasing reliance on complex IT infrastructure has created unprecedented vulnerabilities to both natural and human-induced disasters. Traditional disaster recovery planning in financial institutions has remained largely static, relying on predetermined recovery objectives that fail to account for the dynamic interdependencies and evolving threat landscape characteristic of modern banking ecosystems. This research addresses the critical need for adaptive, intelligent disaster recovery frameworks that can respond to the complex challenges facing contemporary financial institutions. Current approaches to disaster recovery in banking suffer from several fundamental limitations. Most notably, they employ static risk assessment models that cannot adapt to emerging threats in real-time. Additionally, conventional frameworks lack sophisticated dependency mapping capabilities, leading to sub-optimal recovery sequences during actual disaster scenarios. The financial consequences of these limitations are substantial, with industry estimates suggesting that inadequate disaster recovery planning costs the global banking sector approximately $15 billion annually in direct losses and regulatory penalties. This paper introduces a groundbreaking systematic framework that leverages quantum-inspired algorithms and bio-inspired optimization techniques to revolutionize disaster recovery planning in banking IT infrastructure. Our approach represents a paradigm shift from static, predetermined recovery protocols to dynamic, intelligent systems that continuously adapt to changing conditions. The framework's novelty lies in its integration of three innovative components: quantum entanglement-inspired dependency mapping, neural network-based dynamic risk assessment, and swarm intelligence resource allocation.