Adversarial Machine Learning Risks and Defense Strategies for AI-Enabled Cybersecurity Systems in U.S. Cloud Environments

Abstract

The increasing adoption of artificial intelligence (AI) and machine learning (ML) in cybersecurity has created a vulnerability paradox in which systems designed to enhance security also introduce new attack surfaces through adversarial manipulation. This study investigates adversarial machine learning threats targeting AI-enabled cybersecurity systems deployed in United States cloud environments, focusing on Infrastructure-as-a-Service (IaaS), Platform-as-a-Service (PaaS), and Software-as-aService (SaaS) architectures. The research examines major attack classes including evasion, data poisoning, model extraction, and backdoor attacks and evaluates defensive countermeasures such as adversarial training, defensive distillation, ensemble methods, and certified robustness techniques. Using a mixed-methods approach that combines systematic literature review, empirical attack simulations, and comparative defense evaluation across AWS, Azure, and Google Cloud platforms, the study assesses attack success rates, defense effectiveness, computational overhead, and deployment feasibility. Results show that gradient-based adversarial attacks achieve up to 78.3% evasion success against undefended intrusion detection systems, while poisoning only 10% of training data can reduce model accuracy by 34.7%. Adversarial training lowers evasion success to 23.1% but incurs substantial computational cost and accuracy trade-offs, whereas ensemble defenses provide the best robustness–accuracy balance, maintaining 94.2% clean accuracy with 18.7% evasion. The findings highlight persistent challenges, including high resource requirements, adaptive attackers, and limited certified guarantees. This study contributes a cloud-specific adversarial threat taxonomy, empirically validated defense benchmarks, and a practical implementation framework to support risk-informed deployment of robust AI-based cybersecurity systems.