Adversarially Robust Artificial Intelligence: Developing Reliable Intelligent Systems for Critical Applications

Brenda Ortiz

Adversarially Robust Artificial Intelligence: Developing Reliable Intelligent Systems for Critical Applications

Author(s)	Brenda Ortiz
Country	United States
Abstract	Artificial intelligence is increasingly being deployed in critical applications where errors can produce substantial economic, social and safety consequences. However, intelligent systems can be deliberately manipulated through adversarial attacks that exploit vulnerabilities in data, models, software and deployment environments. Adversarially robust artificial intelligence therefore represents an important research direction focused on developing AI systems that maintain reliable performance under malicious, unexpected and distributionally challenging conditions. This paper examines the foundations, attack mechanisms, defence strategies and emerging research directions associated with adversarially robust AI. It discusses adversarial examples, data poisoning, model evasion, backdoor attacks, model extraction, prompt manipulation and other threats affecting machine-learning systems. The paper proposes a layered robustness framework integrating secure data pipelines, robust model architectures, adversarial training, uncertainty estimation, anomaly detection, runtime monitoring, explainability and human oversight. Particular attention is given to critical applications including healthcare, autonomous vehicles, industrial control systems, cybersecurity, financial services and public infrastructure. The study argues that robustness should not be treated as an isolated model-level property but as a system-level capability encompassing data, algorithms, infrastructure, users and operational environments. The paper further highlights challenges involving adaptive adversaries, robustness–accuracy trade-offs, computational cost, transferability of attacks, distribution shifts and the difficulty of evaluating real-world robustness. Future AI systems will require continuous security testing, adversarial evaluation and adaptive defence mechanisms throughout their operational lifecycle. Developing reliable intelligent systems will therefore require a convergence of machine learning, cybersecurity, software engineering, risk management and human-centred system design.
Keywords	: Adversarial AI, Robust Artificial Intelligence, Adversarial Machine Learning, AI Security, Machine Learning Security, Robustness, Adversarial Attacks, Critical Infrastructure, Trustworthy AI, AI Reliability.
Field	Engineering
Published In	Volume 7, Issue 3, May-June 2025
Published On	2025-06-19

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American Journal of Advanced Multidisciplinary Research and Innovation

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Adversarially Robust Artificial Intelligence: Developing Reliable Intelligent Systems for Critical Applications

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