RISK MODELING OF AI-GENERATED CYBER THREATS USING CROWDSOURCED SECURITY EXPERIMENTS

Abstract

The increasing adoption of artificial intelligence across enterprise applications has introduced new cybersecurity challenges associated with AIgenerated threats, automated decision-making, and evolving attack surfaces. Organizations require systematic and ethical methodologies to assess these risks before deploying AI systems in production environments. This paper proposes a risk modeling framework for AI-generated cyber threats using crowdsourced security experiments, integrating machine learning, probabilistic risk assessment, cloud-native DevSecOps, behavioral analytics, human-in-the-loop validation, continuous monitoring, and enterprise governance. The proposed methodology enables security researchers and authorized participants to conduct controlled defensive evaluations, identify AI-related security risks, quantify threat likelihood, assess operational impact, and validate mitigation strategies. Experimental evaluation demonstrates improvements in risk prediction accuracy, governance efficiency, defensive validation coverage, operational reliability, and enterprise cyber resilience. The proposed framework provides a scalable and production-ready solution for responsible AI risk assessment while supporting regulatory compliance and trustworthy enterprise AI deployment. Keywords— AI Risk Modeling, Cybersecurity, Crowdsourced Security Evaluation, Machine Learning, Behavioral Analytics, DevSecOps, Enterprise Governance, Threat Assessment.