AI-Enabled Technical Exposure Risk (ATER): From Public Technical Disclosures to Exposure Intelligence for Cybersecurity Resilience

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.