The rise of connected devices and the growing complexity of cyber threats like advanced persistent threats (APTs) and zero-day vulnerabilities have made traditional, signature-based network security models outdated. This report explores how artificial intelligence (AI) is reshaping cyber defense into a proactive, adaptive, and autonomous approach. It breaks down the core ideas behind AI-driven Dynamic Threat Intelligence (DTI) and Automated Incident Response (AIR), which allow for real-time analysis and rapid threat mitigation. The report also dives into essential AI and machine learning (ML) models, from behavioral analysis to deep reinforcement learning, that are driving this change. It showcases practical, real-world applications, such as using digital twins for safe simulations and the capabilities of commercial platforms like Darktrace. Lastly, it critically examines the technical, operational and ethical challenges we face, including adversarial AI attacks, the black box problem, and data-driven bias, while also looking ahead to the future of AI-native Security Operations Centers (SOCs), where human expertise is enhanced by autonomous AI agents. This paper serves as a structured guide for researchers and practitioners, laying out a roadmap for building more resilient, intelligent, and ethical cyber defense systems.
AI isn’t just a minor upgrade to traditional security; it’s a game changer that completely reshapes how we protect our networked systems. By moving from a reactive, signature-based defense to a proactive, adaptive and predictive approach, AI brings the speed and scale we need to tackle the growing complexity of modern cyber threats. Concepts like Dynamic Threat Intelligence (DTI) and Automated Incident Response (AIR), especially when fueled by cutting-edge AI models such as GNNs and deep reinforcement learning, form a seamless, closed-loop system that continuously learns and acts on its own. That said, the journey toward fully autonomous cyber defense is packed with serious technical and ethical hurdles. We need to tackle the vulnerabilities of AI systems to adversarial attacks, the operational headaches caused by false positives and the ethical questions surrounding accountability, bias and privacy. This calls for ongoing research and the creation of strong frameworks. A balanced approach that harnesses AI’s speed and scale while keeping human oversight and expertise for strategic decision-making and ethical governance is essential. The future of AI in cybersecurity isn’t about replacing human analysts; it’s about enhancing their capabilities, leading to the development of more resilient, intelligent, and ethical cyber defense systems that can genuinely secure our increasingly interconnected world.
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How to Cite This Paper
Deepak Tomar, Ritu Masandra, Kismat Chhillar, Sanchit Agarwal (2026). AI for Dynamic Threat Intelligence and Automated Response in Networked Systems. International Journal of Computer Techniques, 13(2). ISSN: 2394-2231.
