Autonomous Incident Handling in Regulatedfinancial Systems: A Closed-loop Ai Frameworkfor Kubernetes Environments

Software-as-a-Service (SaaS) applications based on Kubernetes platforms require extremely high availability, rapid response to outages, and adherence to regulatory and governance guidelines. Although the application of AIOps technologies has significantly enhanced the process of identifying anomalies and correlating the alerts, in most cases, the production scenarios rely on the human-based remediation process to a significant extent. This dependency results in higher mean time to recover (MTTR), variability of operation and scaling challenge, particularly in complex, distributed workloads. The most one can do to ensure the full autonomy of remediation is in controlled financial affairs due to the rubrics of governance, auditability, explainability, and operational safety. In the current paper, a closed-loop autonomous incident management AI model will be presented on the foundation of the compliance-aware closed-loop AI model in Kubernetes-based financial systems. The framework integrates anomaly identification, decision making through context, automatic remediation, post-action verification and ongoing learning within a feedback loop of control. The immediate ways of integrating regulatory requirements into the automation process are safety gates, confidence thresholds, auditable decision logs, rollback mechanisms and controlled human escalation pathways. The framework is exercised through production telemetry and controlled fault injection across representative financial SaaS workloads. Empirical data suggests a significant reduction in the MTTR and service-level agreement (SLA) violation as compared to the conventional human interaction-driven Site Reliability Engineering processes, with no impact on the correctness of resolution, system stability or even regulatory compliance. It has also been found that the system is effective in automating low risk Level 1 and Level 2 incidents and directing complex or ambiguous incidents to highly skilled engineers. The proposed framework demonstrates that operational autonomy and operations and transparency don’t contradict one another. The paper provides a workable and scaleable roadmap to production level, compliance sensitive AIOps deployment within regulated financial environments.