Evaluating Cybersecurity Compliance Through Legal Jurisprudence: A Machine Learning Approach to Predicting Court Decisions

The importance of cybersecurity compliance in the legal realm is growing as regulatory frameworks adapt to emerging threats. The collection of court decisions relating to cybersecurity compliance is a valuable dataset for understanding judicial patterns and projecting potential outcomes. Using legal jurisprudence and gradient boosting machines (GBMs), this work presents a novel approach to evaluating cybersecurity compliance. GBMs are chosen because of their excellent performance in handling complex, unbalanced datasets and their ability to integrate organised legal features with unstructured text from legal texts. The proposed methodology identifies key factors that influence court decisions in cybersecurity cases by looking at previous court rulings, statutory references, and compliance results. The model's accuracy is verified by cross-validation techniques, and it does a good job of categorising assessments about fines, punishments, and compliance results. Additionally, by emphasising the key characteristics affecting model predictions, interpretability techniques like SHAP (SHapley Additive exPlanations) are utilised to increase decision-making transparency. This machine learning-driven study was created to assist legislators, compliance officers, and legal professionals in anticipating legal trends and enhancing strategic decision-making in cybersecurity compliance. The work demonstrates that by precisely predicting court decisions while maintaining explainability, GBMs can close the gap between complex data-driven techniques and legal interpretation.