AI-Driven Threat Detection and Mitigation in Cloud Infrastructure: Enhancing Security through Machine Learning and Anomaly Detection

Abstract

There are many edges of cloud computing coming into play, which include flexibility and scalability and pave the way for cost efficiency. But it has brought with it huge security problems given the growing increase and scale of cyber threat complexity and sophistication. Despite the success of traditional security mechanisms in protecting many corporations, including the global organization, rule-based Intrusion Identification Systems (IDS) and firewalls tend to be ineffective in preventing attacks by zero day exploits and anomalous behaviors that do not conform to pre-defined signatures. Recently, Cloud infrastructure security has been enhanced by the usage of Artificial Intelligence (AI), in particular, Machine Learning (ML) and anomaly detection. QnA Machine: AI-driven security systems understand threats and take proactive mitigation on this basis. These are potential threats, pattern recognition, behavioural analysis, and predictive analytics. In this paper, we review how AI is integrated into cloud security, how it can be compared to traditional security mechanisms, and analyze the main performance metrics based on which effectiveness of AI-driven systems could be considered. It also presents use cases of such security solutions in the real world and discusses challenges with AI-based security solutions. Future research directions on the aspects of AI-driven threat detection are concluded.