DESIGN OF AI-POWERED CYBERSECURITY THREAT DETECTION SYSTEMS TO PROTECT BUSINESS NETWORKS AND DIGITAL INFRASTRUCTURE FROM EMERGING CYBER RISKS

Schneider, Lukas
Fischer, Hannah
Becker, Jonas

International Journal of Business, Law and Political Science
Vol. 2 No. 12 (2025): International Journal of Business, Law and Political Science

Objective: This paper presents the design and implementation of an AI-powered cybersecurity threat detection system that leverages deep learning and behavioral analysis to identify and mitigate emerging cyber risks. Method: Our proposed architecture combines convolutional neural networks for malware detection, recurrent neural networks for anomaly detection in network traffic, and reinforcement learning for adaptive threat response. Results: Evaluation on benchmark datasets and real-world deployment scenarios demonstrates a threat detection accuracy of 99.2% with an average response time of 45 milliseconds. The system effectively addresses zero-day attacks and advanced persistent threats, providing robust protection for enterprise digital assets. Novelty: The evolving landscape of cyber threats poses significant challenges to business networks and digital infrastructure worldwide.