Economically, tourism supports local communities by generating jobs, stimulating small businesses, and preserving cultural heritage. It plays a crucial role in sustainable development, promoting eco-friendly practices and responsible travel. Safety concerns remain a significant challenge, particularly for vulnerable groups. Artificial Intelligence (AI) has the capability to process large volumes of data in real time, automatically detect risky behaviors or situations involving people, and generate proactive alerts that enable a rapid response. Additionally, AI can reduce human errors, operate continuously, scale across large areas, and adapt to specific environmental contexts, making it an effective tool for enhancing security in spaces such as tourist areas, educational institutions, or urban environments. This study proposes an AI-based real-time security monitoring system for Violet Tourism, leveraging facial recognition, person detection, and spoofing detection to ensure the safety of tourists in designated areas. The system integrates Deep Learning techniques for facial recognition, emotion recognition, and dangerous object detection to provide a proactive security framework. We compared facial detection frameworks using the WIDER FACE dataset and found RetinaFace to be superior due to its advanced architecture and image processing methodology. Additional studies using spoofing detection models yielded positive results in controlled environments. The next step is to extend these evaluations to real-world settings. The implementation of facial recognition in tourism can enhance customer experience but ethical and privacy considerations are crucial. Preliminary tests with a You Only Live Once (YOLO) real-time object detection model showed high performance in controlled environments.