*Result*: Human factors in implementing machine learning-based security measures in clouds.

*Adopting ML-based security measures in cloud-based applications has benefits and cons. The study explores the complex relationship among human characteristics or the implementation of security measures in cloud computing, highlighting the importance of integrating methods to tackle both technological & human-related challenges. The research's main aims are to investigate how human variables affect machine learning-based cloud security, pinpoint roadblocks, and offer optimal integration strategies. The purpose of this study is to improve knowledge of the ways in which corporate culture, individual behaviors, or biases affect the efficacy of different security measures. The findings indicate that human factors, including change aversion, a lack of awareness of safety, privacy issues, or accountability challenges, are important factors in the integration process. To address these challenges, this study underlines the importance of developing a safety-focused society, integrating human factors into security policy, and providing continual education retraining. Integrating ML-based security precautions on cloud-based needs a comprehensive approach that recognizes the value of human factors. Organizations may increase the accuracy of these advanced approaches while resolving human-centric issues by cultivating an ethical environment & resolving privacy & regulatory issues. [ABSTRACT FROM AUTHOR]*