Integrating Big Data Analytics and Devsecops: Adaptive LLM-Based Workflow for Resilient Multi-Cloud Environments

The convergence of Big Data analytics and DevSecOps practices is reshaping the way enterprises design, secure, and scale cloud-native systems. Multi-cloud deployments, while offering flexibility and redundancy, introduce new challenges in resilience, security, and compliance. Traditional DevSecOps pipelines are limited in their ability to adapt to dynamic threats and complex cross-cloud environments. To address this gap, we propose an adaptive workflow that integrates Large Language Models (LLMs) into DevSecOps pipelines, enabling real-time analysis of logs, anomaly detection, automated policy enforcement, and resilience-driven orchestration. Our framework leverages Big Data analytics to process high-velocity telemetry streams while LLM-based agents provide contextual recommendations, automated remediation, and compliance-aware adaptations across AWS, Azure, and GCP. Experimental evaluation demonstrates that the proposed system improves threat detection accuracy by 18%, reduces remediation latency by 27%, and enhances cross-cloud resilience under failure scenarios. This work establishes a foundation for resilient, AI-driven DevSecOps pipelines capable of sustaining security and compliance in complex multi-cloud ecosystems.