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Cyber-Resilient Deep Reinforcement Learning Framework for Multi-Cloud API Modernization and Intelligent Enterprise Integration

Lakshmipurapu Seemaa Sumana Sree

Cybersecurity Analyst, Gauteng, South Africa

Abstract

Modern enterprise IT infrastructures are increasingly migrating toward multi-cloud ecosystems to avoid vendor lock-in, enhance high availability, and maximize operational efficiency. However, integrating legacy enterprise systems with cloud-native APIs across heterogeneous cloud environments introduces significant challenges in dynamic load management, cross-cloud latency, and adaptive security enforcement. Traditional heuristic routing and static policy rules fail to efficiently adapt to fluctuating traffic patterns, leading to suboptimal resource utilization, increased latency, and elevated egress costs. This paper proposes a unified multi-cloud architecture driven by Deep Reinforcement Learning (DRL) for intelligent API modernization and enterprise integration. By deploying a DRL agent using Proximal Policy Optimization (PPO) and Deep Q-Networks (DQN) within an enterprise API Gateway layer, the framework dynamically learns traffic distribution policies, optimizes cross-cloud API routing, and enforces adaptive security governance in real time. The model processes real-world environmental state vectors—including inter-cloud network latency, compute instance pricing, system throughput, and error rates—to compute optimal routing actions that balance performance, cost, and security compliance. Experimental evaluations across a hybrid multi-cloud testbed (AWS, Azure, and Google Cloud) demonstrate that the proposed DRL-enabled platform substantially lowers API response latency, minimizes egress bandwidth expenses, and reduces service-level agreement (SLA) violations compared to traditional round-robin and static weight-based load balancers.

Article Information

Journal

International Journal of Science, Research and Technology

Volume (Issue)

Vol. 8 No. 6 (2025): International Journal of Science, Research and Technology (IJSRAT)

DOI

https://doi.org/10.15662/IJSRAT.2025.0806020

Pages

15440-15448

Published

December 17, 2025
Copyright

Open Access

This work is licensed under a Creative Commons Attribution 4.0 International License.

How to Cite

Lakshmipurapu Seemaa Sumana Sree (%2025). Cyber-Resilient Deep Reinforcement Learning Framework for Multi-Cloud API Modernization and Intelligent Enterprise Integration. International Journal of Science, Research and Technology , Vol. 8 No. 6 (2025): International Journal of Science, Research and Technology (IJSRAT) , pp. 15440-15448. https://doi.org/10.15662/IJSRAT.2025.0806020

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