IJSEA Volume 13 Issue 12

Evaluating AI-Enabled Real-Time Security Architectures for Critical Infrastructure: An Empirical Multi-Domain Study

Eria Othieno Pinyi
10.7753/IJSEA1312.1015
keywords : Artificial Intelligence; Critical Infrastructure Security; Real-Time Cybersecurity; Security Architecture; Cyber Resilience; Multi-Domain Evaluation

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The accelerating digital transformation of critical infrastructure has increased the complexity of protecting interconnected operational technology (OT), information technology (IT), industrial control systems (ICS), and cyber-physical environments from sophisticated cyber threats. Traditional security architectures, characterized by static rule-based mechanisms and delayed incident response, are increasingly inadequate against advanced persistent threats, ransomware, insider attacks, and AI-driven adversarial techniques targeting sectors such as energy, healthcare, transportation, finance, telecommunications, and water utilities. Artificial intelligence (AI) has consequently emerged as a strategic enabler of adaptive, predictive, and autonomous cybersecurity, providing continuous threat intelligence, behavioural analytics, anomaly detection, and automated response capabilities. This study empirically evaluates AI-enabled real-time security architectures across multiple critical infrastructure domains to determine their effectiveness in enhancing cyber resilience, operational continuity, and security decision-making. Using a comparative multi-domain research design, the study assesses key performance indicators including threat detection accuracy, response latency, false-positive rates, scalability, interoperability, and resilience against evolving attack vectors. The findings demonstrate that AI-driven security architectures significantly outperform conventional security models by enabling faster detection, intelligent risk prioritisation, and coordinated incident response while maintaining operational efficiency across heterogeneous environments. However, implementation challenges relating to data quality, model explainability, regulatory compliance, interoperability, adversarial machine learning, and governance remain significant barriers to widespread adoption. The study proposes an integrated evaluation framework that supports the secure deployment of scalable, trustworthy, and resilient AI-enabled security ecosystems capable of strengthening national critical infrastructure protection and advancing cyber resilience in increasingly interconnected digital environments.
@artical{e13122024ijsea13121015,
Title = "Evaluating AI-Enabled Real-Time Security Architectures for Critical Infrastructure: An Empirical Multi-Domain Study ",
Journal ="International Journal of Science and Engineering Applications (IJSEA)",
Volume = "13",
Issue ="12",
Pages ="104 - 117",
Year = "2024",
Authors ="Eria Othieno Pinyi"}