AI THREAT DETECTION FOR CLOUD FILE SHARING: A REAL-TIME SECURITY FRAMEWORK
Cloud file sharing has become a normal part of how people work today. But this convenience brings serious security problems. Traditional antivirus and firewall systems were not built for modern cloud environments where files move quickly between many users and devices. After working with three mid- sized companies for over eight months, we observed that most security teams struggle to detect malicious files shared through Google Drive, SharePoint, and similar platforms. This paper presents a practical AI-based solution that we built and tested in real cloud environments. Our system uses a combination of three machine learning methods — Random Forest, Gradient Boosting, and a small neural network — working together to scan files quickly without slowing down users. We achieved 94.7% accuracy on real-world data, with each file taking less than 100 milliseconds to analyze. The system also explains why it flagged something as suspicious, which helps security people trust the alerts and respond faster. Based on our pilot deployments, organizations saw about 65% fewer successful malware infections through cloud sharing channels
Rituraj, (2026). AI Threat Detection for Cloud File Sharing: A Real-Time Security Framework. International Journal of Science, Strategic Management and Technology, 02(05). https://doi.org/10.55041/ijsmt.v2i5.211
Rituraj, . "AI Threat Detection for Cloud File Sharing: A Real-Time Security Framework." International Journal of Science, Strategic Management and Technology, vol. 02, no. 05, 2026, pp. . doi:https://doi.org/10.55041/ijsmt.v2i5.211.
Rituraj, . "AI Threat Detection for Cloud File Sharing: A Real-Time Security Framework." International Journal of Science, Strategic Management and Technology 02, no. 05 (2026). https://doi.org/https://doi.org/10.55041/ijsmt.v2i5.211.
2.Singh and P. Patel, ”Gradient boosting for PDF malware detection: A comparative study,” in Proceedings of the International Conference on Cyber Security (ICCS), 2023, pp. 145-158.
3.Chen, L. Wang, and H. Zhang, ”Visual malware classification using CNN on binary images,” IEEE Transactions on Dependable and Secure Computing, vol. 21, no. 2, pp. 891-906, 2024.
4.Wang and S. Lee, ”RNN-based malware detection using API call sequences,” Computers & Security, vol. 125, Article 103045, 2023.
5.Li and Y. Zhang, ”User behavior analytics for cloud storage insider threat detection,” ACM Transactions on Privacy and Security, vol. 26, no. 3, Article 22, 2023.
6.Lundberg and S. Lee, ”A unified approach to interpreting model predictions,” in Advances in Neural Information Processing Systems (NeurIPS), 2017, pp. 4765-4774.
7.AV-TEST Institute, ”Malware statistics report 2024,” Technical Report,Cloud Security Alliance, ”Cloud file sharing security best practices,” CSA Research Publication, 2023