AI Driven Kubernetes Certificate Rotation and Secret Governance

Abstract

Kubernetes has become the de facto control plane for modern cloud-native systems, yet its security foundations remain brittle at scale particularly in the management of certificates and secrets. In large enterprises, certificate rotation and secret governance are still treated as operational afterthoughts, implemented through fragmented automation, static policies, or manual interventions. These approaches fail under conditions of scale, organizational complexity, and continuous change, resulting in recurring outages, security incidents, and audit gaps. This paper introduces a risk-aware, AI-driven certificate rotation and secret governance framework for Kubernetes environments, designed as a first-class systems control plane rather than a collection of scripts or tools. The proposed framework integrates continuous telemetry, predictive risk modeling, policy-aware decisioning, and human-in-the-loop governance to autonomously manage certificate lifecycles while preserving accountability and compliance. Unlike existing approaches that rely on time-based rotation or reactive alerts, the system reasons over operational context, workload criticality, trust boundaries, and historical failure patterns to determine when, how, and whether to rotate credentials safely. We present a layered architecture and closed-loop lifecycle model that can be deployed in real-world enterprise platforms, and we evaluate its impact using operational metrics such as mean time to detect (MTTD), mean time to rotate (MTTR), outage reduction, and governance drift. The results demonstrate that intelligent, policy-driven automation can significantly reduce security toil and failure risk without sacrificing human oversight. This work reframes certificate rotation from a mechanical security task into a governed, adaptive systems problem.