Cognitive Digital Twins For Cyber Defense: Self-Learning Ai Agents Against Emerging Threat Landscapes

Abstract

The sophistication of cyber threats has exposed the weaknesses of traditional defense mechanisms. Fixed models and signature-based detection often fail to keep up. In contrast, adaptive artificial intelligence (AI) defenses can learn and evolve in tandem with the evolving threat environment. This paper explains why Cognitive Digital Twins (CDTs) offer a paradigm to develop self-learning AI agents. These agents continually adjust as part of cyber defense systems. CDTs are intelligent, virtual representations of networked environments. They enable proactive simulation, detection, and response to emerging attacks. As cognitive architectures become integrated with digital twins, these models can reason, make decisions, and mitigate threats autonomously. The paper also outlines how to design and test CDT-based defenses using reinforcement learning, anomaly detection, and adaptive feedback loops. A comparative analysis shows that CDT agents outperform conventional intrusion detection and prevention systems. They excel in detection accuracy, response speed, and resistance to unknown attacks. The results highlight the potential for self-learning AI agents to revolutionize cybersecurity practices, based on detection rates, false positive rates, and adaptability. Finally, the paper discusses implications for enterprise security, critical infrastructure protection, and future research on adaptive AI for cyber resilience.