An Integrated Cybersecurity Program Management Framework for Mitigating Adversarial AI Threats in Medium and Large Organizations

Abstract

The increasing adoption of artificial intelligence (AI) in organizational cybersecurity introduces both advanced defensive capabilities and new vulnerabilities, particularly from adversarial attacks targeting machine learning models. Traditional cybersecurity program management frameworks, while effective for conventional threats, often lack mechanisms to address AI-specific challenges, leaving organizations exposed to sophisticated attacks. This study proposes an integrated, multi-layered cybersecurity framework that combines governance, risk management, real-time anomaly detection, and adversarial resilience strategies. Using an Isolation Forest–based anomaly detection model implemented in a Python environment, the framework is evaluated on synthetic datasets simulating adversarial behaviors. Experimental results demonstrate high detection accuracy (96.8%), low false positive rates (3.2%), and robust response times suitable for real-world deployment. Comparative analysis with baseline models confirms superior performance and resilience under adversarial scenarios. The proposed framework provides organizations with a scalable, adaptive, and intelligent approach to manage cybersecurity risks in AI-driven environments.