AI-Driven Cybersecurity Threat Detection Using Machine Learning and Data Analytics in Cloud Computing Environments

Abstract

Cloud computing has made its way into the daily operations of businesses, changing the way they store, manage and share data, and at the same time expanding the scope, complexity, and dynamism of cyber threats.Cloud computing has been adopted by many organisations and impacts the way in which organisations store, process and share information while growing the size, complexity and dynamism of cyber threats. Cloud environments feature distributed infrastructure, virtual resources, interdependent services, ever-evolving workloads, and massive amounts of data concerning security. These qualities are problematic for detection, often beyond the ability of traditional rules-based, signature-driven and/or manually monitored cybersecurity strategies. Targeted attacks can be hard to detect when they display new attack patterns, rapidly evolve, leverage legitimate cloud services or are introduced on multiple interconnected services. Within this context, artificial intelligence (AI), machine learning (ML) and data analytics have grown in importance as solutions to enhance intelligent and adaptive cybersecurity threat detection.The study focuses on qualitative analysis of the role of AI‐based techniques, specifically machine learning and data analytics, in cybersecurity threat detection for cloud computing environments. The method used in the study is interpretive, exploratory, and documentary research method which is secondary evidence research. Relevant evidence is conceptually synthesized from peer reviewed scholarly literature, systematic and review studies, cybersecurity research, cloud-security publications, industry and security reports, standards, technical documents and authoritative institutional sources. Instead of building and testing a new machine-learning model, this study uses the existing knowledge to determine key patterns, challenges, opportunities, and practical implications of cloud threat detection using AI.The five areas identified using thematic analysis are interlinked. First, AI and ML can help detect more intelligently by detecting complex patterns of behavior and potentially even recognizing anomalies that might not be identified through traditional methods. Second, data analytics allows for analysis of diverse cloud-security data across networks, applications, devices, workloads, logs, and other distributed sources. Third, AI-powered solutions provide possibilities to deal with new and advanced threats using adaptive and behaviour-based detection. Fourth, they are affected by the quality of the data, false positive rates, explainability, privacy, security of training data, and organizational trust. Last but not least, when AI is integrated with other components of the cloud-security ecosystem, such as monitoring, incident-response, and security operations centers, it can enhance security operations in real time and adapt to the changing nature of threats.The study offers a conceptual analysis of how AI-enabled cybersecurity detection is a more holistic socio-technical ability than an algorithmic one. In practice, the findings indicate that organizations and cybersecurity practitioners should assess different types of AI-based detection based on the quality and variety of data they have, the interpretability of security decisions, their ability to adapt to new threats, their privacy policies, their operational context, and how well they fit into their current security infrastructure. In summary, the success of AI-driven threat detection in cloud computing relies on a combination of advanced algorithms and reliable data sources, transparent decision-making processes, adaptability, privacy-focused measures, and seamless integration with cloud-security frameworks. This holistic view can help in creating more resilient, responsive, and intelligent cybersecurity strategies in today's more complex cloud environments.