Customer support is one of the key aspects of users’ experience across online services. However, with the emergence of natural language processing technologies, industries are now exploring chatbot solutions to provide high-quality automated chat services to users. This thesis presents a practical case study of such a chatbot solution that supports information security for those interested in this field. First, the specific goal of the chatbot and the current and previous techniques for developing automated chat programs were discussed. The theories behind deep learning techniques, recurrent units, and neural networks were explained. A list of the problems solved by the chatbot was compiled. Next, a scalable software architecture was proposed and explained, followed by data analysis and a thorough examination of neural network structures for classifying user intent. It was discovered that recurrent units are the most effective for classification. Finally, after evaluating the results, it was determined that the chatbot’s behavior is satisfactory but requires some standards modification to improve its performance. The chatbot program underwent training, optimization with various settings, and enhancement with additional features to ensure ease of use. Customer interaction and feedback were evaluated, demonstrating its significant effectiveness. The chatbot delivers excellent results and proves to be efficient in addressing customer inquiries, resolving cybersecurity concerns, and providing appropriate instructions.