A Semantic Audit Framework for National Security Reports Driven by Structural Empty Graph Networks (SEGN)

Annual public reports issued by national security agencies serve concurrently as factual disclosures, explanatory mechanisms, and governance statements. Existing methodologies—including topic modelling, keyword co-occurrence analysis, and general information extraction—often struggle to align ‘assertion-evidence-mechanism-causality’ within a unified framework, thereby failing to establish verifiable inferential loops. This deficiency constitutes a knowledge gap in semantic auditing. Taking the 2025 Annual Report of Latvia's Constitution Protection Bureau (SAB) as its subject, this paper proposes a three-tier digital product roadmap driven by SEGN (Structured Empty Graph Network): It employs Single Source of Truth (SSOT) lossless graph binding to link page numbers with evidence fingerprints. The computable reasoning layer structures the concept-mechanism-causality chain while introducing evidence alignment and coverage thresholds (τ_a, τ_c). Finally, the audit interface records audit status (Pass/Warning/Fail) and uncertainty through field-based documentation. Results indicate that within this report context, mechanism nodes such as ‘legal warfare pathways, cybersecurity compliance implementation, and classified information protection governance’ occupy pivotal positions in structural centrality and evidence density. Furthermore, under specific conditions, enhanced evidence coverage facilitates mechanism inference migration from warning to pass levels, explainable through an ‘evidence alignment-threshold escalation’ audit mechanism. These findings support transforming national security texts from readable narratives into sets of inferential objects that are replayable, threshold-based, and falsifiable. This provides a unified language for inferential strength across annual comparisons and document migrations, while offering verifiable foundations for evidence-based risk control in public security and cyber governance.