Modern enterprise systems increasingly rely on distributed, cloud-native, and microservice-based architectures, where service continuity is critical for operational resilience. However, the complexity of multi-cloud environments introduces a high probability of failures arising from configuration drift, resource contention, security vulnerabilities, and unpredictable workload spikes. Traditional rule-based recovery mechanisms and static monitoring systems are insufficient for diagnosing and resolving such failures in real time. This paper proposes a cognitive diagnostic framework for automated enterprise service recovery using generative artificial intelligence (GenAI), integrating probabilistic reasoning, model-based inference, and adaptive decision-making mechanisms.
The study builds upon foundational theories in information systems validation, model selection, and reliability engineering to construct a unified perspective on intelligent system recovery. Core principles from statistical model selection (Burnham and Anderson, 2002) and factor-based inference (Akaike, 1987) are extended to dynamic cloud environments for anomaly detection and root cause analysis. Additionally, empirical insights from cloud reliability failures and outages in enterprise systems (Charette, 2010; Charette, 2011) are used to motivate the need for proactive cognitive diagnostics.
The proposed framework incorporates generative AI models capable of interpreting system telemetry, logs, and dependency graphs to infer causal failure chains. It further leverages Kubernetes-based orchestration principles and self-healing mechanisms as a foundation for automated recovery strategies. The work emphasizes the integration of post-mortem intelligence systems to continuously refine diagnostic accuracy through feedback loops derived from system recovery outcomes (Post-Mortem Intelligence for Self-Healing Multi-Cloud Enterprise Applications Using LLMs and Kubernetes, 2026).
The results of this conceptual synthesis indicate that cognitive diagnostics significantly improve recovery time objectives (RTO) and reduce mean time to resolution (MTTR) in complex cloud ecosystems. However, challenges remain in interpretability, data heterogeneity, and computational overhead. This paper contributes a structured theoretical foundation and architectural model for next-generation autonomous enterprise recovery systems powered by generative AI.