Cybersecurity Workforce Upskilling: CRISP-DM Automation with Jupyter Notebooks and Immersive Lab Terraform Modules

Sandhya Guduru

Abstract: The rapid evolution of cybersecurity threats necessitates continuous workforce upskilling, with automation playing a crucial role in enhancing training effectiveness. This research explores the integration of the Cross-Industry Standard Process for Data Mining (CRISP-DM) framework with Jupyter Notebooks for automating threat modeling and security analysis. Additionally, it examines the use of Immersive Lab Terraform modules to provide hands-on cloud attack simulations, bridging the gap between theoretical knowledge and practical application. By aligning these methodologies with CyberSeek Heatmap metrics, this approach ensures targeted skill development that meets industry demands. The study highlights the potential of automation-driven training pipelines in equipping cybersecurity professionals with the necessary expertise to mitigate evolving threats.

Keywords: Cybersecurity Workforce Upskilling, CRISP-DM, Jupyter Notebooks, Threat Modeling, Immersive Lab Terraform Modules, Cloud Security, CyberSeek Heatmap, Security Automation, Hands-on Training, Skill Gap Analysis