Hybrid Cybersecurity: AI Models for Predicting Threats Across Multi-Cloud for U.S. Business Environment

Abstract

The presence of multipurpose cloud solutions has presented emerging cybersecurity concerns and an incentive to use more sophisticated methods, including threat prediction and mitigation. The above writing presents the integration of artificial intelligence (AI) hybrid models in multipurpose cloud infrastructures to achieve better threat prediction and detection. The hybrid AI models, achieved by implementing supervised and unsupervised learning algorithms in parallel with the fusion of data across multiple cloud platforms help to achieve new heights of accuracy, scalability, and flexibility of the cybersecurity systems. The study mentions use of deep learning, extensive data management and reinforcement learning as a real-time detection mechanism not just to detect known threats but also detect new threats. The findings indicate that hybrid AI solutions could be relevant in achieving substantial performance in terms of the threat detection rate, low false alarms, and quicker response rates in running multipurpose cloud setups. These findings confirm again the strength of hybrid AI models to cope with the dynamic nature of cybersecurity threats and, hence, enhance the system resilience and offer more efficient protection in various cloud environments. The article analyzes the embodiment of artificial intelligence (AI) models to their optimal in multi-cloud entities to enhance its security risk detection and prediction measures. Both the integration of the guided and uncontrolled learning approaches as well as the integration of various cloud data and the use of hybrid AI approach allow for the increase in precision and flexibility of threat detection systems. Thus, based on the analysis presented in the paper, it becomes clear that there are certain ways of applying AI to identify both known and newly formed threats. These AI methods include such approaches as deep learning, reinforcement learning, and anomaly detection. The application of hybrid AI models allows for the successful identification of changes within the dynamic multi-cloud environment.