Cybersecurity in Digital Transformation: Using AI to Automate Threat Detection and Response in Multi-Cloud Infrastructures

Abstract

The accelerating pace of digital transformation has led organizations to increasingly adopt multi-cloud infrastructures, which offer scalability, flexibility, and cost efficiency. However, these infrastructures also introduce significant security challenges, particularly in terms of managing and mitigating the expanding attack surface. The complexity of securing such environments, coupled with the volume and sophistication of cyber threats, has rendered traditional security mechanisms inadequate. In response, artificial intelligence (AI) has emerged as a transformative technology, capable of automating threat detection and response processes, thereby enhancing security postures and reducing incident response times in multi-cloud environments. This paper investigates the application of AI in automating cybersecurity within multi-cloud infrastructures during digital transformation, exploring its ability to detect, analyze, and respond to sophisticated threats in real-time.

The first part of the research focuses on the critical security challenges posed by multi-cloud infrastructures, particularly the heterogeneity of cloud platforms, disparate security controls, and the need for consistent visibility across environments. These challenges exacerbate the difficulty of threat detection and response, which is further compounded by the lack of centralized security governance and the increased vulnerability of cloud-native applications. The paper examines how the dynamic nature of cloud services, such as autoscaling and resource allocation, introduces security risks that traditional methods fail to adequately address.

AI-driven threat detection systems leverage advanced machine learning (ML) algorithms, neural networks, and deep learning models to identify anomalous behavior and detect potential threats across multi-cloud environments. The research delves into how AI models can be trained to analyze vast amounts of data generated from various cloud platforms, such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP), to detect threats in real time. By integrating AI into security information and event management (SIEM) systems, organizations can automate the process of correlating logs, identifying patterns indicative of malicious activity, and reducing false positives. Furthermore, the paper discusses how AI can enhance the accuracy and speed of intrusion detection systems (IDS) and intrusion prevention systems (IPS) in multi-cloud environments, allowing for proactive defense mechanisms.