Graph learning is well suited to modeling relationships among communicating entities in network intrusion detection. However, the resulting models are frequently difficult to interpret, in contrast to many classical approaches that offer more transparent reasoning. This work integrates SHapley Additive exPlanations with temporal, edge-aware GNN based on GraphSAGE architecture to deliver an explainable, inductive intrusion detection model for NetFlow data named TE-G-SAGE. Using the NF-UNSW-NB15-v3 dataset, flow data are transformed into temporal communication graphs where flows are directed edges and endpoints are nodes. The model learns relational patterns across two-hop neighborhoods and achieves strong recall under chronological evaluation, outperforming a GCN baseline and recovering more attacks than a tuned XGBoost model. SHAP is adapted to graph inputs through a feature attribution on the two-hop computational subgraph, producing global and local explanations that align with analyst reasoning. The resulting attributions identify key discriminative features while revealing shared indicators that explain cross-class confusion. The research shows that temporal validation, inductive graph modeling, and Shapley-based attribution can be combined into a transparent, reproducible intrusion detection framework suited for operational use.

Public technical disclosures support transparency, certification, procurement, interoperability, and cybersecurity governance, but AI-assisted open-source intelligence (OSINT) can make dispersed records easier to connect and reuse. This article introduces AI-Enabled Technical Exposure Risk (ATER), a disclosure-review framework for deciding how public technical detail should be reported when aggregation and linkage increase its security relevance. The framework is developed through a cutover-bounded corpus of public EUCC/Common Criteria certification artifacts available through the ENISA certificate portal by 15 April 2026. This study combines conceptual framing, descriptive corpus screening, and pilot feasibility checking of the disclosure-review workflow. The results show that public certification artifacts can contain recurring traceability signals, candidate technology linkages, version or scope uncertainty, evaluation-boundary indicators, and related security-relevant disclosure patterns that become more visible when reorganized at aggregate level. A practical disclosure-risk flowchart was derived from the study codebook and pilot-calibrated against evidence-tier and disclosure-decision categories in the corpus. The analysis characterizes aggregate publication-review patterns rather than inferring product-specific vulnerabilities. The resulting model offers authors, evaluators, and governance teams a structured basis for routing technical detail toward aggregate reporting, abstraction, contextual caveating, internal evidence retention, or further legal, certification, or security review.