Secure Multi-Cloud DevOps Architecture with AI-Driven Threat Detection and Automated Infrastructure Resilience

Abstract

The increasing adoption of multi-cloud strategies in enterprises has enhanced scalability, operational flexibility, and service availability. However, it has also introduced complex security challenges, including distributed attack surfaces, inconsistent policy enforcement, and vulnerability management across heterogeneous environments. Traditional security mechanisms are insufficient for dynamic multi-cloud infrastructures, necessitating intelligent and automated approaches to ensure system resilience.
This research proposes a secure multi-cloud DevOps architecture integrated with AI-driven threat detection and automated infrastructure resilience mechanisms. The framework leverages machine learning and artificial intelligence models to monitor network traffic, system activity, and application behavior in real time, enabling proactive identification of anomalies and potential threats. Automated resilience modules, including self-healing, fault-tolerant orchestration, and dynamic resource scaling, mitigate the impact of attacks or failures without manual intervention.
By integrating AI-based cybersecurity with cloud-native DevOps practices, the architecture ensures secure, scalable, and highly available multi-cloud environments. Continuous monitoring, automated policy enforcement, and predictive threat intelligence provide enterprises with enhanced operational efficiency, regulatory compliance, and infrastructure reliability. The research outlines the architectural principles, implementation strategies, and evaluation metrics for deploying a multi-cloud DevOps platform capable of adapting to evolving security threats and maintaining resilient enterprise operations.