AI-Augmented Data Privacy Risk Assessment Framework for Smart Governance Applications

With the rapid digitalization of public services, privacy concerns in smart governance ecosystems have escalated, necessitating robust and interpretable risk assessment frameworks. This paper presents PrivacyX-Net, an AI-augmented architecture designed for automated multi-dimensional privacy risk evaluation from policy documents. The framework begins by encoding annotated clauses using a fine-tuned Privacy-Bert, which captures domain-specific contextual semantics. These representations are structured into a hierarchical graph, where a Hierarchical Graph Attention Network (HGAT) models inter-clause relationships. A Risk-Aware Attention Layer fuses outputs from both the semantic and graph-based streams to amplify high-risk contexts. Finally, a multi-label classifier predicts granular privacy risk scores across dimensions such as data collection, information sharing, data retention, and user control. The model was validated on the OPA-PolicyPrivacy dataset, achieving an F1-score of 0.89 and a Hamming loss of 0.061, outperforming existing baselines such as LSTM (F1 = 0.81) and GCN+BERT (F1 = 0.86). Extensive ablation studies and efficiency evaluations confirmed the effectiveness and scalability of the proposed architecture. Additionally, the model offers interpretability through attention visualization, supporting transparency in governance-driven decision-making. PrivacyX-Net thus enables automated, modular, and explainable privacy risk assessment, contributing significantly to the development of accountable digital infrastructures.