Enhancing Cybersecurity Resilience with AI-Powered Threat Detection Systems

Abstract

Cybersecurity is a major concern across sectors given the increasing complexity of digital threats. This study evaluates the application of an AI-powered threat detection system to improve an organization’s cybersecurity resilience. By leveraging technologies such as Machine Learning (ML) and Deep Learning (DL), the system is able to detect new threat patterns and respond in real-time. The study shows that the AI-powered system has an accuracy rate of up to 95% in detecting threats, reducing the average response time from 4 hours to less than 30 minutes, and reducing false positives by 40%. The results also revealed that AI can detect 87% of new, unregistered threats. However, the adoption of this technology faces challenges, such as high implementation costs, reliance on quality data, and the risk of AI-based adversarial attacks. The study recommends mitigation strategies, including adversarial-based training, careful data management, and investment in AI infrastructure. The study concludes that the application of AI provides an adaptive and effective solution to improve cybersecurity resilience despite the challenges that must be overcome.