The rapid expansion of cloud-native architectures has increased dependence on service accounts, workload identities, application credentials, API keys, tokens, certificates, and other non-human identities (NHIs). Unlike conventional human identities, NHIs frequently operate continuously, interact programmatically with multiple services, and may possess privileges that are difficult to associate with a clearly accountable individual. Consequently, static access-control models are increasingly inadequate for environments in which identity privileges, workload behavior, and infrastructure dependencies change dynamically. This paper proposes a risk-aware automated governance framework for service accounts, workload identities, and machine credentials within cloud Identity and Access Management (IAM). The framework integrates identity discovery, contextual risk assessment, privilege analysis, lifecycle governance, credential rotation, behavioral monitoring, policy enforcement, and automated remediation. Methodologically, the study develops a conceptual governance model by synthesizing principles of automated protection and governance of non-human identities with algorithmic optimization, classification, feature-selection, and validation perspectives represented in the supplied literature. The resulting framework treats identity governance as a continuous risk-management process rather than a periodic administrative activity. The analysis indicates that risk-aware automation can reduce unmanaged credentials, constrain excessive privilege, improve credential lifecycle visibility, and enable adaptive security decisions. However, automation introduces challenges involving false positives, policy conflicts, operational disruption, explainability, and dependency on accurate identity metadata. The proposed framework therefore combines automated decisions with policy constraints, confidence thresholds, and human oversight for high-impact actions.