A Unified Cybersecurity Program Management and AI Adversarial Defense Framework for Enhancing Organizational Resilience in Medium and Large Enterprises

Abstract

The increasing reliance of medium and large organizations on digital infrastructures and AI-driven systems has expanded the cybersecurity threat landscape, exposing enterprises to sophisticated attacks. Traditional approaches, focusing on compliance and static controls, are insufficient to address dynamic threats, particularly adversarial attacks targeting AI models. This study proposes a unified cybersecurity framework that integrates program management principles with AI adversarial defense mechanisms. The framework adopts a layered architecture, combining governance, risk management, AI security, operational execution, and intelligence-driven adaptation. A quantitative risk model is developed to assess both conventional and AI-specific threats, while Python-based simulations demonstrate effective risk prediction and adversarial detection. Experimental results show high detection accuracy, robust risk assessment, and enhanced operational efficiency. The proposed framework bridges the gap between strategic cybersecurity governance and technical AI security, providing a scalable, adaptive, and future-ready solution for organizational resilience. This research contributes to advancing proactive cybersecurity strategies by unifying management-level oversight with AI-specific threat mitigation, ensuring enterprises remain resilient against evolving digital threats.