The rapid expansion of cloud-native applications, automated pipelines, microservices, containers, serverless workloads, and machine-to-machine communication has increased the number and operational importance of non-human identities. Unlike conventional human accounts, machine and workload identities may be created, rotated, delegated, and terminated automatically, making their governance difficult to manage through manually administered identity and access management (IAM) processes. This research develops a policy-based automation framework for secure governance of machine and workload identities in cloud IAM. The proposed approach integrates identity lifecycle controls, policy evaluation, contextual authorization, anomaly detection, automated credential governance, continuous compliance assessment, and risk-adaptive enforcement into a unified control model. The methodology conceptually combines policy-based access control with automated decision mechanisms inspired by optimization, machine learning, authentication, and anomaly-detection techniques represented in the supplied literature. Particular attention is given to the governance requirements of non-human identities, including least privilege, identity provenance, credential rotation, workload isolation, policy consistency, and automated revocation. The analysis indicates that policy automation can reduce administrative dependency, improve consistency, accelerate detection and response, and establish measurable governance controls across dynamic cloud environments. However, automation introduces risks associated with erroneous policy decisions, excessive privileges propagated through service dependencies, model-driven false positives, and policy complexity. The study therefore positions automation not as a replacement for governance but as an enforcement mechanism operating within explicit policy boundaries. The resulting framework provides a research-oriented foundation for secure, scalable, and continuously governed machine and workload identities in cloud IAM.