The IBM 2024 Cost of a Data Breach Report highlights that cloud environments are the initial attack vector in 39% of breaches, with lateral movement observed in nearly one-third of incidents, increasing detection and containment times. Lateral movement in cybersecurity refers to techniques used by cybercriminals to navigate an infected network, identify vulnerabilities, elevate access rights, and achieve their objectives, such as ransomware attacks, cyber espionage, data exfiltration, and botnet infection. Attackers employ various techniques like Pass-the-Hash attacks, internal spear phishing, and remote service exploitation to move laterally. The process includes stages such as infection, compromise, reconnaissance, and credential theft. Detecting lateral movement requires mapping potential routes, leveraging reporting tools, monitoring unknown devices, and analyzing user behavior. Preventive measures include implementing a zero-trust security model, updating software, enforcing the principle of least privilege, and using multi-factor authentication. In Kubernetes environments, lateral movement involves compromising a single container or pod and moving across the cluster, necessitating real-time visibility and in-cluster enforcement to mitigate risks. Cast AI's Runtime Security provides detection and response capabilities for unauthorized lateral movement within Kubernetes clusters, using anomaly detection powered by CEL-based rules.