The paper introduces a machine learning-based intelligent security framework for secure cloud key management, titled the Secure Policies of Cloud Security Framework (SPCSF). This framework aims to enhance the security and reliability of key management services in cloud environments, which are crucial for protecting sensitive data. The SPCSF consists of two main components: a permission detection engine and a registration authorization engine. The permission detection engine ensures that applications have legitimate permissions by analyzing permission manifests, byte codes, and cross-referencing with a list of sensitive APIs. The registration authorization engine facilitates secure application registration and access control, using a safe authentication technique to grant or deny access to REST APIs based on the level of risk they pose. The framework addresses the challenges of cloud security, including unauthorized access, data breaches, and the need for robust policies and frameworks. It leverages machine learning and baseline deviation detection to proactively identify anomalies, thereby safeguarding critical resources and sensitive data. The paper also highlights potential applications and use cases across different industries, emphasizing the importance of secure key management in cloud environments.The paper introduces a machine learning-based intelligent security framework for secure cloud key management, titled the Secure Policies of Cloud Security Framework (SPCSF). This framework aims to enhance the security and reliability of key management services in cloud environments, which are crucial for protecting sensitive data. The SPCSF consists of two main components: a permission detection engine and a registration authorization engine. The permission detection engine ensures that applications have legitimate permissions by analyzing permission manifests, byte codes, and cross-referencing with a list of sensitive APIs. The registration authorization engine facilitates secure application registration and access control, using a safe authentication technique to grant or deny access to REST APIs based on the level of risk they pose. The framework addresses the challenges of cloud security, including unauthorized access, data breaches, and the need for robust policies and frameworks. It leverages machine learning and baseline deviation detection to proactively identify anomalies, thereby safeguarding critical resources and sensitive data. The paper also highlights potential applications and use cases across different industries, emphasizing the importance of secure key management in cloud environments.