With the continuous increase in the degree of complexity of cyber threats, signature-based approaches traditionally used for the detection of malware and the identification of security vulnerabilities are unsuitable. The results of the research indicate that, given the increased degree of complexity of cyber threats, traditional signature-based approaches for malware detection and security vulnerability evaluation are inadequate. Based on research results, artificial intelligence and machine learning can improve cybersecurity defenses by better malware detection and threat identification. Organizations can create developing prediction models that can identify new hazards and match changing attack strategies by means of advanced machine learning techniques. AI systems can process big data in real time, therefore enabling patterns and anomalies that might otherwise go unseen to be discovered. Furthermore, emphasized in this study are the need of high-quality labeled datasets, reduction of false positives, and guaranteeing model interpretability. Artificial intelligence-driven cybersecurity can also address the future hazards such zero-day exploits and enhanced persistent attacks. Being a hybrid model, the Convolutional Neural Network (CNN) and Bidirectional Long Short-Term Memory (BiLSTM) model provides a robust architecture for advanced sequence-based tasks, particularly in intrusion detection and cybersecurity applications.