The escalating complexity of cyber threats has created an urgent need for intelligent, adaptive, and autonomous defense mechanisms that can evolve alongside adversarial strategies. To address these challenges, this paper introduces the concept of Cognitive Cyber Twins (CCT)—a dual-agent framework that emulates human-like cognition for dynamic network defense and data integrity assurance. The proposed twin-agent model comprises a Physical System Twin (PST) that continuously monitors operational networks and a Cognitive Decision Twin (CDT) that leverages artificial intelligence to analyze, predict, and mitigate potential intrusions in real time. Through a synergistic learning loop, the CDT adapts its defense strategies based on environmental context, behavioral anomalies, and historical attack patterns, thereby enabling proactive and resilient cybersecurity operations. Experimental evaluations on simulated network datasets demonstrate that the proposed CCT framework significantly enhances detection accuracy, reduces false positive rates, and maintains high data consistency even under complex attack scenarios. Comparative analysis with existing security systems further validates the superiority of the cognitive twin approach in terms of adaptability and decision precision. This work establishes a foundational step toward intelligent, self-healing, and context-aware network defense architectures, paving the way for future integration of autonomous twin-based security agents in large-scale cyber infrastructures.

Keywords: Cognitive Cyber Twins, AI-Augmented Security, Twin-Agent Framework, Adaptive Defense, Data Integrity, Autonomous Cyber Systems