This paper presents a machine learning-based intelligent security framework for secure cloud key management (SPCSF). The framework aims to enhance the security and reliability of key management services in cloud environments by implementing precise control over application permissions and encrypting REST API communications. The framework consists of two main components: (i) a permission detection engine that verifies the legitimacy of application permissions by analyzing permission manifests, byte codes, and cross-referencing permissions against a list of sensitive APIs. (ii) A registration authorization engine that enables secure application registration with the controller using a suggested authentication technique, allowing or denying access to REST APIs based on the risk level of the application. This approach ensures secure and authorized access to critical resources. The paper highlights the importance of secure key management in cloud environments, emphasizing the need for robust policies and frameworks to protect sensitive data and prevent unauthorized access, data breaches, and the compromise of confidential information. It discusses the challenges of cloud security, including the risks associated with data storage and the need for effective access control mechanisms. The paper also addresses the diversity of access management systems across different cloud platforms and open-source projects, which adds complexity to the cloud security landscape. The SPCSF framework is designed to enhance the security and integrity of interactions between applications and a key management service (KMS). By leveraging machine learning and baseline deviation detection, it proactively identifies anomalies, thereby safeguarding critical resources and sensitive data. The framework ensures that registered applications can access the KMS in an authorized and secure manner, reducing the likelihood of unauthorized access or potential security breaches. Ultimately, it contributes to a robust security infrastructure, instilling confidence in the system’s reliability and compliance, especially in scenarios involving multi-user mobile applications and sensitive data handling.This paper presents a machine learning-based intelligent security framework for secure cloud key management (SPCSF). The framework aims to enhance the security and reliability of key management services in cloud environments by implementing precise control over application permissions and encrypting REST API communications. The framework consists of two main components: (i) a permission detection engine that verifies the legitimacy of application permissions by analyzing permission manifests, byte codes, and cross-referencing permissions against a list of sensitive APIs. (ii) A registration authorization engine that enables secure application registration with the controller using a suggested authentication technique, allowing or denying access to REST APIs based on the risk level of the application. This approach ensures secure and authorized access to critical resources. The paper highlights the importance of secure key management in cloud environments, emphasizing the need for robust policies and frameworks to protect sensitive data and prevent unauthorized access, data breaches, and the compromise of confidential information. It discusses the challenges of cloud security, including the risks associated with data storage and the need for effective access control mechanisms. The paper also addresses the diversity of access management systems across different cloud platforms and open-source projects, which adds complexity to the cloud security landscape. The SPCSF framework is designed to enhance the security and integrity of interactions between applications and a key management service (KMS). By leveraging machine learning and baseline deviation detection, it proactively identifies anomalies, thereby safeguarding critical resources and sensitive data. The framework ensures that registered applications can access the KMS in an authorized and secure manner, reducing the likelihood of unauthorized access or potential security breaches. Ultimately, it contributes to a robust security infrastructure, instilling confidence in the system’s reliability and compliance, especially in scenarios involving multi-user mobile applications and sensitive data handling.