Kubernetes has progressively become a central technology for the orchestration of containerized workloads in cloud environments, but its dynamic, multi-layered architecture significantly increases the complexity of security operations. Existing Kubernetes security tools typically focus on isolated domains such as vulnerability scanning, configuration assessment, runtime detection, or network observability. Despite their effectiveness in their respective domains, these solutions often generate fragmented outputs that lack correlation, contextualization, and alignment with threat-informed and Cyber Threat Intelligence (CTI)-oriented representations, which are increasingly required to maintain an effective and proactive security posture, thus complicating risk assessment and prioritization. This thesis examines the current landscape of Kubernetes security tools and identifies key limitations, including contextual fragmentation, limited cross-layer correlation, and the lack of threat-informed representations of cluster security data.