<b>Analyzing Enterprise Data Protection and Safety Risks in Cloud Computing Using Ensemble Learning</b>

Nowadays, cloud computing is a significant advancement in the information technology sector. Cloud computing manages and distributes large amounts of data and resources on the internet. In the IT sector, it is used significantly for accessing IT infrastructure via a computer network without necessitating local installations on individual devices. Protecting data security and privacy in cloud computing has become a major issue.In this study, we used ensemble machine learning algorithms for the analysis of cloud computing data. Our focus lies in the analysis of features that affect data security and privacy threats in cloud computing. Data was gathered through online and physical survey methods. The data-gathering method involved various industry professionals' interactions. The survey dataset features consist of security challenges faced by organizations, such as organization size, industry sector, types of data managed, existing security measures, and prevalent security challenges.The primary focus was on evaluating the effectiveness of three machine learning classifiers: Decision Tree, Random Forest, and Support Vector Machine (SVM), which achieved accuracies of <b>85.4%</b>, <b>89.6%</b>, and <b>88.2%</b>, respectively. To enhance predictive accuracy and robustness, an ensemble learning approach using a voting classifier was implemented, resulting in a significantly improved accuracy of <b>91.5%</b>. The results show that ensemble learning outperforms individual classifiers in predicting cloud data security threat concerns.This paper highlights significant insights for academics and practitioners by implementing ensemble learning approaches that significantly strengthen cloud computing security measures, making them more robust against possible attackers.