AI-Driven Decision-Making Models in Engineering Systems: Implications for Cybersecurity and System Reliability

Artificial Intelligence (AI) has emerged as a transformative force in engineering systems, offering advanced decision-making capabilities that enhance system reliability and cybersecurity. This study examines the implications of AI-driven models, focusing on their ability to improve operational efficiency and safeguard against cyber threats. Using descriptive and inferential statistical methods, the research evaluates the performance of various AI models, including neural networks, support vector machines, and decision trees. Results indicate significant improvements in system uptime, reduced failure events, and enhanced cybersecurity breach detection, with neural networks demonstrating superior accuracy and reliability. However, challenges such as system complexity and data management highlight the need for optimized designs and robust cybersecurity frameworks. This research emphasizes the critical role of model selection and ethical considerations in deploying AI for engineering systems, paving the way for more resilient and efficient technological advancements.