Open Threat Database Integration and Volunteer-Driven AI Safety Initiatives as a Model for Hybrid Threat Intelligence Architecture in Commercial Cybersecurity Platforms

Anton Kulyk

Citation: Anton Kulyk, "Open Threat Database Integration and Volunteer-Driven AI Safety Initiatives as a Model for Hybrid Threat Intelligence Architecture in Commercial Cybersecurity Platforms", Universal Library of Innovative Research and Studies, Volume 03, Issue 03.

Copyright: This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

The article examines a hybrid threat intelligence architecture for commercial cybersecurity platforms that integrates open threat databases, industry sources, and volunteer-driven AI safety initiatives. The purpose of the study is to substantiate a three-tier model that combines governmental vulnerability databases, community-based standards, and sources addressing artificial intelligence incidents. The relevance of the research is determined by the expansion of the contemporary threat surface, where CVE-based vulnerabilities, web application risks, supply chain weaknesses, and AI-related incidents require a unified analytical environment. The novelty of the article lies in its interpretation of volunteer-driven AI safety initiatives as an independent component of commercial threat intelligence infrastructure. Using VULNWatch as a case study, the article demonstrates that the combined use of NVD, OWASP, the AI Incident Database, and fourteen integrated tools expands risk coverage, reduces dependence on single-source feeds, and supports alignment with NIST CSF, NIST AI RMF, ISO/IEC 27001, and ISO/IEC 42001. The article will be useful for researchers, developers of cybersecurity platforms, AI governance specialists, and information security auditors.


Keywords: Threat Intelligence, Open Data Integration, AI Safety, National Vulnerability Database, OWASP, AI Incident Database, Hybrid Architecture, AI Governance, Vulnerability Assessment.

Download doi https://doi.org/10.70315/uloap.ulirs.2026.0303005