In cloud‑native environments, where identities, services, and data flows change constantly, signature‑based detection alone cannot keep pace with modern attacks. Behavior‑based threat‑detection systems instead monitor runtime activity—API calls, network flows, and user actions—to build baselines of normal behavior and flag anomalies. Machine learning models correlate logs from Kubernetes, cloud services, and application traces to surface subtle indicators such as unusual outbound traffic, credential‑spray‑like access patterns, or lateral‑movement‑style API calls. When integrated with automated response workflows, these systems can isolate compromised workloads, rotate secrets, and trigger incident‑response playbooks in near‑real time. By focusing on “what is happening” instead of “what rule was matched,” organizations gain resilience against zero‑day and credential‑based attacks.