AI-Powered Intelligent Disaster Recovery and File Transfer Optimization for IBM Sterling and Connect:Direct in Cloud-Native Environments

Abstract

In modern enterprise environments, Managed File Transfer (MFT) systems such as IBM Connect:Direct and IBM Sterling File Gateway play a critical role in enabling secure, reliable, and high-volume data exchange. However, traditional disaster recovery mechanisms for these systems are reactive, relying on manual failover or static rule-based triggers, which can lead to prolonged downtime and data loss. This paper presents a novel AI based predictive disaster recovery framework specifically designed for IBM MFT systems operating across hybrid and multi cloud environments. The proposed approach employs a Long Short Term Memory (LSTM) neural network trained on real time telemetry data including transfer logs, resource utilization metrics, and system errors to forecast potential system degradation or failure. Upon detecting early warning signs, the framework automatically provisions recovery nodes using Infrastructure as Code (IaC) with Terraform and replicates critical configurations through secure cloud orchestration tools such as Google Cloud Functions and Cloud DNS. In addition, a traffic aware routing layer optimizes ongoing file transfers based on current load, network bandwidth, and SLA requirements. The system has been evaluated using simulated failure scenarios and real world transfer workloads, demonstrating significant improvements in failover latency, transfer continuity, and operational cost efficiency. This work represents the first application of deep learning based predictive recovery in IBM MFT systems and offers a scalable, proactive alternative to conventional disaster.