The rapid evolution of cyber threats has exposed the limitations of traditional signature-based intrusion detection systems (IDS), particularly in identifying novel and sophisticated attacks. In response, this study explores an AI-driven intrusion detection framework that leverages deep learning techniques to enhance cybersecurity defenses. The proposed approach integrates denoising autoencoders for robust feature representation, convolutional neural networks (CNNs) for capturing local traffic patterns, and long short-term memory (LSTM) networks for modeling temporal dependencies in network behavior. Using benchmark intrusion detection datasets, the framework is evaluated against classical machine learning models, including support vector machines and random forests, employing standard performance metrics such as accuracy, precision, recall, F1-score, and AUC-ROC. The experimental results demonstrate that the deep learning–based ensemble significantly outperforms traditional approaches, particularly in detecting low-frequency and zero-day attacks while maintaining a reduced false-positive rate. The study also discusses deployment challenges, including class imbalance, model interpretability, and computational overhead. Overall, the findings confirm that deep learning–enabled IDS architectures offer a scalable and adaptive solution for modern cybersecurity environments, providing improved threat detection capabilities in increasingly complex network infrastructures.