The rapid digitalization of engineering and technological systems has significantly increased exposure to sophisticated cyber threats. Traditional security mechanisms often fail to detect complex and previously unknown cyberattacks in a timely manner. This study explores artificial intelligence–based engineering solutions for the early detection and prevention of cyberattacks in digital infrastructures. The research employs analytical review, comparative analysis, and experimental modeling to evaluate machine learning and deep learning approaches applied to intrusion detection and threat prediction. The results demonstrate that AI-driven cybersecurity systems outperform traditional signature-based methods in terms of detection accuracy, adaptability, and response time. The findings highlight the importance of integrating artificial intelligence into modern engineering cybersecurity architectures to enhance system resilience and proactive defense capabilities.

cybersecurity, artificial intelligence, intrusion detection, machine learning, engineering systems, cyber threat prevention.

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