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

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