Cloud-Native AIOps Architecture for Secure Observability and Automated Incident Response in Multi-Enterprise Environments
DOI:
https://doi.org/10.15662/IJARCST.2024.0706037Keywords:
cloud-native computing, AIOps, observability, automated incident response, cybersecurity, multi-enterprise environments, machine learning, anomaly detection, distributed systems, DevSecOps, intelligent operations, security automationAbstract
Cloud-native environments have transformed enterprise information systems by enabling elastic computing, distributed applications, containerized workloads, and continuous software delivery. However, the resulting complexity makes conventional monitoring and manually coordinated incident management increasingly difficult. Artificial intelligence for IT operations (AIOps) provides an opportunity to integrate observability data, machine learning, automation, and security controls to identify operational anomalies and accelerate incident response. This paper proposes a cloud-native AIOps architecture for secure observability and automated incident response across multi-enterprise environments. The architecture integrates distributed telemetry collection, centralized and federated analytics, machine-learning-based anomaly detection, event correlation, service dependency analysis, security monitoring, policy enforcement, and automated remediation. It is designed to process metrics, logs, traces, events, and security signals generated across heterogeneous enterprise environments while preserving organizational boundaries and access controls. The methodology evaluates the proposed architecture using controlled operational and cybersecurity scenarios involving workload failures, performance degradation, configuration errors, anomalous behavior, and security incidents. Key evaluation criteria include detection accuracy, mean time to detect, mean time to respond, false-positive rate, remediation success, resource consumption, scalability, and security-policy compliance. The study emphasizes human oversight for high-impact remediation while enabling automated responses for well-defined and reversible incidents. The proposed framework provides a foundation for combining cloud-native observability, AIOps, and security automation into an adaptive operational intelligence system capable of supporting resilient and secure multi-enterprise digital infrastructures
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