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The growing sophistication of cyber threats has reduced the effectiveness of traditional cybersecurity tools in protecting modern organisations and complex networks. This challenge requires advanced solutions capable of real-time detection, rapid response, and efficient threat mitigation. In this context, AI-based approaches have emerged as a powerful enabler of intelligent, adaptive, and data-driven security operations. This study presents a comprehensive analysis of AI-driven threat detection combined with automated incident response mechanisms in modern cybersecurity architectures. The novelty of this work lies in the integration of advanced machine learning-based detection with real-time, automated response capabilities to address zero-day and previously unknown threats in heterogeneous digital environments. The paper examines system architecture design, implementation strategies, and performance evaluation across diverse deployment scenarios. Experimental results demonstrate that AI-driven detection with automated response significantly enhances cybersecurity effectiveness, achieving accuracies between 96% and 97%, dramatically reducing the mean response time from 45 min to less than 30 s, and substantially improving zero-day threat detection and containment success rates. Overall, the proposed approach achieves up to a 98.9% improvement in incident containment efficiency, highlighting the operational and defensive advantages of intelligent automation.
Tanimu et al. (Mon,) studied this question.