Event Forensics–Inspired Automated Recovery Strategies for Enterprise-Scale Distributed Services Employing Generative Artificial Intelligence

Abstract

The increasing complexity of enterprise-scale distributed services has intensified the challenge of maintaining service availability, reliability, and operational resilience. Modern cloud-native architectures comprise interconnected microservices, containerized workloads, orchestration platforms, and heterogeneous infrastructure components that collectively generate vast quantities of operational telemetry. Traditional incident response mechanisms frequently rely on manual diagnostics, predefined rules, and reactive recovery procedures, resulting in extended downtime and operational inefficiencies. Recent advancements in Generative Artificial Intelligence (GenAI), particularly large language models (LLMs), provide new opportunities for transforming incident management into intelligent, adaptive, and self-improving recovery ecosystems.

This research proposes an event forensics–inspired automated recovery framework for enterprise-scale distributed services that integrates forensic event analysis, contextual knowledge extraction, causal reasoning, and generative decision support. The proposed framework utilizes operational artifacts generated during failures, including logs, traces, metrics, alerts, and configuration histories, to construct structured forensic intelligence. This intelligence is subsequently analyzed through generative models to identify probable root causes, recommend corrective actions, and orchestrate automated recovery workflows. Unlike conventional monitoring systems that emphasize anomaly detection alone, the proposed approach leverages historical incident knowledge and contextual reasoning to create adaptive recovery mechanisms capable of learning from previous disruptions.

The study develops a conceptual architecture composed of event acquisition, forensic correlation, knowledge representation, generative reasoning, and automated remediation layers. Drawing upon principles of human-like decision making, cognitive modeling, risk-based adaptation, and behavioral learning from the literature, the framework introduces a novel perspective in which distributed systems emulate expert operational reasoning during incident response. The findings suggest that event forensics combined with generative intelligence can significantly reduce mean time to detection (MTTD), mean time to resolution (MTTR), and operational uncertainty while improving service resilience.

The paper contributes a comprehensive theoretical and methodological foundation for integrating forensic analytics and generative AI into enterprise recovery systems. The proposed framework demonstrates how organizations can transition from reactive operational management toward autonomous resilience capable of continuous adaptation in dynamic cloud-native environments.