DYNAMIC RISK ASSESSMENT IN CLOUD ENVIRONMENTS USING AI-DRIVEN BIG DATA TECHNIQUES

Abstract

This broad study analyzed danger request models and cloud security models as well as the befuddling subject of AI execution in the security domain. Strangely, we found proof of a positive connection between's the size of the training dataset and the model's ensuing presentation in the field of cloud security. Moreover, notwithstanding the way that dataset variety considerably affected execution, an intriguing model was noticed: bigger datasets would by and large show less assortment. By zeroing in on danger request, our examination uncovered the reasonable benefits of various AI models. Specifically, Brain Associations ended up being exceptionally viable in recognizing Phishing dangers, while Decision Trees ended up being profoundly enticing in distinguishing Malware. This exhaustive comprehension of model suitability across numerous security issues uplifts our consciousness of the extensive variety of AI applications in security settings.