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