The increasing sophistication and frequency of cyber threats has led to a critical need for advanced cybersecurity solutions and skilled professionals. In order to provide students with useful skills for handling contemporary cybersecurity challenges, this paper presents the design and implementation of a Data-Driven Cybersecurity Lab (DDCS), a cloud-based platform that combines data-centric approaches, machine learning (ML), and artificial intelligence (AI). We determined key elements for cybersecurity education, including data management, practical experience, and AI-driven threat detection, by examining industry requirements and current cybersecurity curricula. In order to give students a hands-on learning environment where they can apply data-driven techniques to real-world scenarios, the DDCS lab was developed using cloud infrastructure and IBM Auto AI. Students constructed machine learning models for intrusion detection using the NSL-KDD dataset, proving the lab’s usefulness in bridging the gap between theoretical knowledge and real-world application. The lab equips students for the quickly changing cybersecurity landscape by allowing them to work with large-scale datasets, create predictive models, and automate threat detection. This study demonstrates how the DDCS lab can be an essential teaching resource for preparing the upcoming generation of cybersecurity experts.