Investigating the Feasibility and Risks of Leveraging Artificial Intelligence and Open Source Intelligence to Manage Predictive Cyber Threat Models

Abstract

This study investigates the integration of Artificial Intelligence (AI) and Open Source Intelligence (OSINT) to enhance predictive threat modeling in cybersecurity, addressing the growing complexity and frequency of cyber threats. Integrating AI and OSINT offers transformative potential by enabling organizations to transition from reactive to proactive security measures, a critical need in the evolving digital landscape. Leveraging data from the Twitter Academic API, Common Crawl Dataset, and MITRE ATT&CK Framework, the analysis employed descriptive statistical analysis, logistic regression, and multivariate regression methodologies. Results indicate high data completeness (90.41%) and relevance (81.44%) in OSINT datasets, supporting their suitability for AI model training. Logistic regression demonstrated strong predictive capabilities, achieving 94.98% accuracy, 88.69% precision, and an AUC score of 0.91. However, risks such as data bias (-0.36 coefficient) and adversarial manipulation (-0.33 coefficient) significantly impact predictive performance. The ethical implications of this integration, including concerns about privacy, data fairness, and the potential for misuse, are highlighted as critical considerations for broader adoption. Recommendations include robust preprocessing protocols, advanced adversarial defenses, ethical guidelines, and continuous AI innovation to address these challenges. These findings underscore the potential of AI-OSINT integration while emphasizing the need for ethical and technical safeguards to enhance cybersecurity effectiveness.