Artificial intelligence powered framework for effective threat detection in cloud environments
P. M. Arathi, Sugandha Saxena, B. Kavya, Sarappadi Narasimha Prasad, Jitendra Kumar Jaiswal, Zabeeulla A. N. Mohammed, Raja Praveen K N · 2025
As clouds grow in complexity, traditional security measures are up to the task in spotting complex cyber threats. First, this paper describes my work in building an artificial intelligence (AI) powered framework which adds to the ability to detect threats in cloud infrastructures. The framework uses machine learning (ML) algorithms and anomaly detection technique to automatically detect both known as well as new threats in the system in real time. Being a combination of both supervised and unsupervised learning models, it analyses large volumes of cloud data, finding patterns hidden in it as well as reduces false positives. The framework possesses the adaptivity to adapt to the threat landscape changes while having its high detection accuracy and other properties of efficiency. It also allows for scalability, making it an appropriate platform for running across the dynamic cloud on deployable workloads. But what this approach gets is even stronger cloud security as well as shortening of response time, enabling proactive threat mitigation. The proposed AI-driven framework offers a great advancement to securing modern cloud systems against modern cyber threats.