Deep learning has greatly impacted various fields, including cybersecurity, but it comes with significant challenges and limitations. One major challenge is the need for vast amounts of labeled data for training, which is difficult to obtain due to privacy concerns and the time-consuming nature of data annotation. Additionally, the computational complexity required for training deep learning models necessitates substantial resources, often making it inaccessible for organizations with limited capabilities. Environmental impacts due to energy consumption during model training also raise sustainability concerns. Moreover, the interpretability of deep learning models poses problems as they often operate as black boxes, making it hard for security analysts to understand their decision-making processes. This lack of transparency is crucial in cybersecurity where the rationale behind flagged malicious activities needs to be understood. Furthermore, deep learning models are susceptible to adversarial attacks, which can exploit their sensitivity, leading to incorrect predictions and compromised security. Issues like overfitting also persist, where models excel with training data but fail to generalize to new threats, a critical drawback in the evolving landscape of cyber attacks. Although techniques like regularization and cross-validation exist to mitigate overfitting, finding the right balance between model complexity and generalization remains challenging. Lastly, ethical and privacy concerns further complicate the deployment of deep learning in cybersecurity, necessitating careful consideration and management.