Judicial decision-making in continental law systems requires carefully evaluating complex, interdependent evidence while ensuring consistency and fairness. This study investigates the application of Bayesian networks in structuring legal evidence within an AI-based decision support system. The primary research objective is to assess the enhancement of transparency, minimization of cognitive overload, and reduced bias in judicial processes through probabilistic reasoning. The proposed system dynamically updates outcome probabilities as new evidence is introduced, enabling real-time monitoring of case likelihoods. AI-generated recommendations are aligned with judicial precedents by integrating Conditional Probability Tables (CPTs) and historical case data in this adaptive approach. The interpretability and effectiveness of Bayesian inference in legal decision support are analyzed methodologically, emphasizing its capacity to refine probability distributions in response to evolving courtroom inputs. The findings address a key research gap by demonstrating how structured AI-driven heuristics can supplement judicial reasoning while maintaining decision accountability and transparency. The system is suggested to enhance consistency and fairness in legal judgments while preserving judicial autonomy. This study contributes to the growing intersection of AI and legal decision-making, with an emphasis placed on the role of machine learning in supporting judicial heuristics while maintaining procedural integrity.

Cyberattacks increasingly generate systemic economic and geopolitical effects in an era of dense global interdependence. While prior research emphasises geopolitical rivalry, institutional capacity, and technological sophistication as determinants of national cyber risk, less attention has been given to structural vulnerabilities arising from countries’ positions within global economic networks. This study advances a relational theory of national cyber risk, arguing that structurally central countries provide greater systemic leverage to attackers because disruptions to highly accessible nodes can propagate widely across interconnected trade systems. Using annual bilateral trade data from 2010 to 2020, we construct directed, weighted global trade networks and derive centrality measures capturing accessibility, brokerage, and embeddedness. These indicators are linked to country-level cyber incident data to evaluate both the probability and intensity of cyberattacks. Logistic and negative binomial models with lagged network metrics show that countries occupying more accessible positions face significantly higher cyberattack risk. The findings demonstrate that national cyber vulnerability emerges from relational exposure within interconnected economic systems, underscoring the importance of systems-based cybersecurity risk assessment.