Abstract

Artificial Intelligence (AI) is reshaping mechanical engineering by enabling automation, predictive maintenance, and intelligent design optimization. These advancements drive efficiency and innovation but also introduce critical cybersecurity challenges, including data integrity risks, adversarial AI threats, and vulnerabilities in cyber-physical systems. The increasing connectivity and reliance on AI-integrated mechanical systems amplify the need for robust security measures to protect against potential cyberattacks. This paper systematically reviews the intersection of AI, mechanical engineering, and cybersecurity, identifying key challenges and advancements. A systematic literature review (SLR) using the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) methodology was conducted, analyzing over 150 peer-reviewed sources. Results indicate that AI-powered predictive maintenance reduces operational downtime by 30%, while cyberattacks targeting AI-driven mechanical systems have surged by 45% in the last five years. Additionally, ransomware attacks in industrial settings have risen by 65%, highlighting the urgent need for enhanced security measures. This paper examines AI’s applications in mechanical engineering, the associated cybersecurity risks, and emerging security frameworks, including blockchain, zero-trust architecture, and AI-driven threat detection systems. The study highlights the need for interdisciplinary strategies that enhance both efficiency and security in AI-powered mechanical systems. Future research should focus on real-time anomaly detection, quantum-resistant encryption, and regulatory compliance frameworks to ensure secure AI-driven mechanical solutions. By addressing existing research gaps, this study provides a foundation for developing resilient AI-integrated mechanical systems that balance innovation with security.