Open Data for Cyber Resilience: An Analysis of Public-private Collaboration in AI-Supported Threat Intelligence Sharing

Abstract

This study explores how open threat intelligence data and artificial intelligence (AI) can jointly enhance cybersecurity resilience and innovation through structured public-private collaboration. As cyber threats become increasingly complex and transnational, the need for coordinated intelligence sharing between government and private institutions has never been more urgent. This research builds on Okunleye’s empirical work linking open data to innovation and extends it into the cybersecurity domain. Using open-access datasets from MITRE ATT&CK, Verizon’s 2024 Data Breach Investigations Report (DBIR), the OECD, and the EU Open Data Portal, the study evaluates how open CTI (Cyber Threat Intelligence) infrastructures enable AI-assisted threat detection, response, and governance. Employing descriptive statistics, multiple linear regression, moderation analysis, and principal component analysis (PCA), the study finds that public institutions consistently outperform private ones in CTI readiness. AI collaboration improves breach detection speed by 25.63 units, reduces incident response time by 52.11 units, and enhances containment effectiveness by 31.24 units. Additionally, AI significantly amplifies the innovation gains derived from public-private collaboration. Key structural barriers identified include legal restrictions, data localization, and technical formatting inconsistencies. The study proposes a Collaborative Cyber Resilience Model (CCRM) that integrates open data standards, AI systems, and regulatory cooperation to support secure and scalable threat intelligence sharing. This research offers practical insights for cybersecurity policymakers, operational leaders, and researchers seeking to understand and implement resilient, AI-enabled CTI frameworks across sectors.