The constantly changing cybersecurity threats require the creation of adaptive decision systems utilizing artificial intelligence (AI). The study presents a new hybrid method that merges Convolutional Neural Networks (CNN) and Gated Recurrent Units (GRU) to improve the precision of cyber threat detection. The system demonstrates a remarkable accuracy of 98.06% when tested on the NSL-KDD dataset, a well-established benchmark in the field. The CNN component is utilized for its effectiveness in extracting spatial features, enabling it to accurately capture complex patterns in network traffic data. The Gated Recurrent Unit (GRU) enhances the model’s capacity to recognize temporal relationships and sequential trends in network activities, which is essential for detecting evolving cyber threats. The hybrid model outperforms traditional standalone models by utilizing the strengths of both CNN and GRU architectures. By combining these neural network components, a resilient and flexible system is formed that can accurately identify a variety of cyber threats. Experiments were carried out to assess the proposed method using the NSL-KDD dataset, a commonly used dataset for intrusion detection. The hybrid CNN-GRU model demonstrated its effectiveness by achieving an accuracy of 98.06% in accurately detecting and categorizing malicious activities in network traffic. Moreover, the system demonstrates a low false positive rate, reducing the likelihood of false alarms and ensuring effective utilization in practical cybersecurity situations. The hybrid decision system demonstrates high accuracy and adaptability to changing threats by continuously learning and updating. AI-driven decision-making in cybersecurity automates threat detection and provides insights to security analysts, fostering a synergistic human-machine partnership for effective cybersecurity defense. This study enhances AI-powered cybersecurity decision systems and highlights the importance of hybrid neural network structures for achieving high accuracy and adaptability against evolving cyber threats.