Intelligent Observability in Cloud-Native Enterprise Applications through Predictive Performance and Causal Trace Mining with Secure AI and ML Pipelines
DOI:
https://doi.org/10.15662/IJEETR.2023.0502008Keywords:
Cloud-Native Architecture, Predictive Performance, Causal Trace Mining, AI/ML Pipelines, Enterprise Observability, Security, Anomaly DetectionAbstract
Modern cloud-native enterprise applications operate in increasingly complex, distributed environments where system performance, security, and reliability are critical. Traditional monitoring and observability methods often fail to detect latent performance issues or identify root causes in real time. This paper proposes an intelligent observability framework for cloud-native enterprise systems that integrates predictive performance analytics, causal trace mining, and secure AI/ML pipelines. The framework leverages machine learning models to predict system bottlenecks, automatically analyze causal relationships between events, and detect anomalies in real time. Federated and secure AI pipelines enable distributed model training without exposing sensitive enterprise data, ensuring compliance with regulatory standards such as HIPAA, GDPR, and PCI DSS. The proposed architecture combines microservices, containerization, and event-driven monitoring to support end-to-end observability across heterogeneous enterprise systems. Experimental evaluation using healthcare, financial, and insurance enterprise datasets demonstrates significant improvements in predictive accuracy, reduced system downtime, and efficient anomaly detection. The results indicate that intelligent observability not only enhances operational reliability but also enables proactive system management, reduces response times to incidents, and strengthens security posture. This study contributes a unified, AI-enabled observability approach suitable for modern cloud-native enterprise applications, bridging gaps between predictive analytics, causal diagnostics, and secure AI operations.
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