Architecting Enterprise Decision Intelligence through Cloud Native Computing and AI Powered Security Analytics (International Journal of Advanced Research in Computer Science & Technology(IJARCST), 2020)
Modern enterprises face unprecedented operational friction when transforming high-velocity streaming data into actionable business strategies without compromising cyber resilience. This paper presents an architectural framework that unifies enterprise decision intelligence (DI) with cloud-native computing and AI-powered security analytics. Traditional monolithic architectures separate business analytics from security monitoring, creating computational silos, latency, and fragmented threat visibility. The proposed framework solves this by embedding containerized AI pipelines into cloud-native microservices, allowing real-time business telemetry and security log processing to share distributed compute resources. By applying deep learning algorithms—such as graph neural networks and attention-based sequence models—the framework enables concurrent zero-day threat detection and contextual business forecasting. Furthermore, the architecture incorporates privacy-preserving mechanisms, including differential privacy and federated learning, ensuring strict regulatory compliance across multi-cloud environments. An event-driven orchestration layer dynamically balances compute workloads during traffic spikes, keeping latency low and processing throughput high. By transforming reactive security logs into proactive decision assets, this unified approach reduces operational risk, improves time-to-insight, and gives enterprise leaders a reliable, real-time foundation for strategic decision-making.
Keywords: Enterprise Decision Intelligence; Cloud Native Computing; AI-Powered Security Analytics; Microservices Architecture; Cyber Resilience; Privacy-Preserving AI; Federated Learning