Citation:

Selva Kumar Ranganathan, 2023. "Intelligent Incident Management: Leveraging AI for Real-Time Root Cause Analysis in DevOps Pipelines", ESP Journal of Engineering & Technology Advancements 3(1): 224-228.

Abstract:

In the age of cloud-native architectures and rapid software delivery, the complexity of managing system reliability has escalated dramatically. This research investigates the integration of Artificial Intelligence (AI) into DevOps workflows to enable intelligent incident management, with a particular focus on real-time Root Cause Analysis (RCA). As Continuous Integration and Continuous Deployment (CI/CD) pipelines scale across microservices, ephemeral environments, and diverse infrastructure layers, the frequency, scope, and cascading impact of production incidents have become more pronounced.Traditional incident response practices rooted in manual log inspection, predefined heuristics, and static rule-based systems are increasingly insufficient. These approaches are often reactive, time-intensive, and prone to human error, especially under pressure. In contrast, AI-driven solutions offer the ability to learn from historical patterns, detect anomalies in real time, and provide contextualized RCA insights autonomously.This paper presents a comprehensive AI-augmented framework that combines machine learning classifiers, time-series anomaly detection (LSTM), natural language understanding (BERT), and graph-based service dependency modeling (GNNs) to streamline incident triage and resolution. The framework ingests multi-modal operational data logs, metrics, alerts, and chat transcripts and correlates it to generate high-fidelity RCA reports with minimal latency. Deployed across three enterprise-grade DevOps environments, the solution demonstrated a 42% average reduction in Mean Time to Resolution (MTTR), a significant decrease in alert noise, and high alignment between AI-generated RCAs and human-validated postmortems.The study also explores critical challenges in real-world deployments, including data sparsity, model drift, explainability, and organizational resistance to AI adoption. Strategies for overcoming these limitations such as phased rollouts, transparent inference trails, and continuous retraining pipelines are detailed. Finally, the paper outlines the future trajectory of AI for IT Operations (AIOps), including autonomous remediation, zero-shot incident detection, and federated learning for cross-environment RCA generalization.This research offers compelling evidence that AI-enhanced incident management is not only feasible but essential for building resilient, scalable, and self-healing software systems in an era of increasing operational complexity.

References:

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Keywords:

DevOps, Incident Management, Root Cause Analysis, Artificial Intelligence, Machine Learning, Time-Series Analysis, NLP, Automation, CI/CD, Observability, LSTM, BERT, Kafka.