CROWDSOURCED ADVERSARIAL AI FRAMEWORK FOR EVALUATING AND MITIGATING AUTONOMOUS CYBERATTACK RISKS

Abstract

Artificial intelligence has significantly transformed cybersecurity by enabling intelligent threat detection, automated vulnerability assessment, and adaptive defense mechanisms. However, increasingly autonomous AI systems also introduce new security risks that require systematic evaluation before deployment. This paper proposes a crowdsourced adversarial AI framework for evaluating and mitigating autonomous cyberattack risks by integrating ethical AI red teaming, machine learning, humanin-the-loop validation, cloud-native security, DevSecOps, continuous monitoring, and enterprise governance. The proposed methodology enables security researchers to safely evaluate AI behaviors, identify potential security weaknesses, assess operational risks, and validate mitigation strategies within controlled environments. Continuous behavioral analysis, automated risk scoring, and collaborative security testing improve AI robustness while supporting responsible AI deployment and regulatory compliance. Experimental evaluation demonstrates improvements in threat detection, defensive validation accuracy, governance efficiency, AI reliability, and enterprise cyber resilience. The proposed framework provides a scalable and production-ready solution for evaluating and mitigating AI-enabled cybersecurity risks in modern enterprise environments.