ASSESSING THE POTENTIAL OF USING ARTIFICIAL INTELLIGENCE AND MACHINE LEARNING MODELS TO ENSURE THE SECURITY OF CLOUD ENVIRONMENTS AND AUTOMATED MANAGEMENT SYSTEMS FOR CONTAINERIZED APPLICATIONS

Abstract

Cloud computing and containerized environments have become foundational components of modern IT infrastructure, offering scalability and agility. However, their dynamic nature introduces significant security challenges, including anomalies in traffic, DDoS attacks, hidden crypto mining (cryptojacking), and credential compromise. Traditional signature-based security mechanisms often fail to address these rapidly evolving threats effectively. The objective of this study is to assess the potential of artificial intelligence (AI) and machine learning (ML) in enhancing cloud and container security. Specifically, it explores the effectiveness of AI/ML models for anomaly detection, threat classification, cryptojacking, and DDoS identification, deception-based defenses, and false positive reduction. The methodology involves a structured literature review of key scientific publications from 2023 to 2025. Comparative analysis is conducted on experimental solutions, including hybrid models (XGBoost, CNN, LSTM) in intrusion detection systems; eBPF-based syscalls tracing for container behavior profiling; ML classifiers for vulnerability prioritization in DevSecOps; and active defense platforms combining honeypots and adaptive monitoring loops (MAPE-K). Findings indicate that AI-powered security systems achieve detection accuracies above 99%, reduce false positive rates to around 2%, and enable real-time responsiveness without degrading system performance. Notably, systems that integrate multiple models and utilize low-level data (e.g., syscalls, network patterns) exhibit superior threat identification and resilience. In conclusion, integrating AI into cloud security architectures is essential for ensuring continuous and proactive defense in dynamic infrastructures. The paper also outlines future research challenges, such as the need for explainable AI (XAI), limited availability of high-quality training datasets, and vulnerability to adversarial inputs. The insights are relevant for cybersecurity researchers and practitioners seeking to deploy intelligent defense mechanisms in cloud-native ecosystems.