# Security Hardening and Compliance Assessment of Kubernetes Control Plane and Workloads
## 2 Related Works
[PERSON] and [PERSON] [12] compared four Kubernetes deployments--two managed services and two on-premises clusters. They used automated tools to assess misconfigurations and vulnerabilities. While insightful, the study was limited by a lack of long-term metrics and variation in cluster configurations. [PERSON] et al. [13] introduced a machine learning-based method for multi-class threat detection in Kubernetes environments. Their hybrid PCA-autoencoder-Naive Bayes model achieved 91% accuracy. Although promising, the model's performance depended heavily on large, labeled datasets and high computational costs, limiting its use in small organizations. [PERSON] and [PERSON] [14] developed a centralized defense solution for detecting Kubernetes misconfigurations using open-source tools. Their system successfully aggregated diagnostic logs. Nevertheless, they acknowledged the challenge of high infrastructure costs and noted that the solution may not scale well in cost-sensitive or resource-limited environments. [PERSON] [15] presented a layered security analysis of Kubernetes components, from containers to clusters. The study mapped vulnerabilities and proposed high-availability strategies. However, the paper was largely theoretical, lacking implementation and testing of the suggested methods in production or simulated Kubernetes environments. [PERSON] and [PERSON] [16] applied the Zero Trust model to Kubernetes networking for a financial services firm. Their network design improved segmentation and control. While simulations showed strong results, the authors noted that deploying their model in production could be difficult due to existing vendor lock-ins and integration constraints. [PERSON] [17] designed a Kubernetes multi-tenancy model for fintech firms that used namespaces, RBAC, and pod security policies. The architecture showed promising results in tests. Still, the authors faced challenges in resource allocation during peak loads and ensuring strict isolation under dynamically changing tenant requirements. [PERSON] et al. [18] revisited Zero Trust principles in Kubernetes for enterprise data centers. They proposed a network design validated through simulations. Although the model adhered to vendor guidelines, compatibility with legacy infrastructure and a lack of vendor-neutral implementations were significant deployment barriers. [PERSON] [19] examined Kubernetes in healthcare IT systems, focusing on secure data processing and infrastructure resilience. While highlighting scalability and automation benefits, he acknowledged issues like data privacy, regulatory compliance, and difficulty integrating with legacy systems in health environments. [PERSON] and [PERSON] [20] assessed automated attack tools targeting Kubernetes, finding them effective in exploiting default configurations. Their study emphasized the risks of improper setup and the steep learning curve faced by operators. However, the research was limited to simulated environments and did not evaluate long-term impacts. These studies demonstrate that Kubernetes security has emerged as a significant and thoroughly examined area within cloud-native computing. Researchers have tackled various challenges, including misconfigurations, threat detection, and the implementation of zero-trust frameworks. Despite these advancements, the domain remains deficient in standardized frameworks, large-scale empirical validation, and holistic solutions for real-time, multi-tenant, and hybrid-cloud security issues, underscoring the necessity for ongoing, thorough inquiry. The following section offers a comprehensive examination of the Kubernetes architecture, establishing the essential foundation for comprehending the components and interactions supporting containerized workloads' orchestration. The following section outlines a detailed security framework encompassing cluster-level protections, container image fortification, identity and access management, and integrated observability via auditing, logging, and monitoring.