GAN-Based Anomaly Detection for Cybersecurity: A Technology Management Framework for Legal Risk, Regulatory Compliance, and Digital Governance

Abstract

The growing complexity of cyber threats has highlighted the shortcomings of traditional signature-based IDS (intrusion detection systems) and led to the need for intelligent, adaptive, and governance-centric cybersecurity solutions. Recent developments in the field of deep learning have produced a novel technique called Generative Adversarial Networks (GANs), which has shown promise in anomaly detection by its ability to learn complex data distributions and detect previously unseen attack patterns. Research on GAN-based cybersecurity to date, however, has been mostly oriented towards enhancing the detection performance, while limited research has been conducted on organizational decision-making, legal risk management, regulatory compliance, and digital governance. This paper presents a holistic GAN Based Anomaly Detection Framework for Cybersecurity, which combines intelligent anomaly detection with technology management, legal risk assessment, regulation compliance and digital governance. The proposed framework consists of four layers that are interconnected: data acquisition and data preprocessing; anomaly detection using the GAN; technology management; and integration of legal risk and compliance. The framework integrates the Advanced Threat Protection (ATP) capabilities of artificial intelligence with the widely adopted cybersecurity standards such as the NIST Cybersecurity Framework (CSF 2.0), ISO/IEC 27001:2022, and the General Data Protection Regulation (GDPR), which can be used for the detection of unusual network activity as well as to prioritize organizational risks, monitor compliance, report to management, and inform strategic decision making. The proposed approach is different from the traditional intrusion detection architecture, in which anomaly detection is seen as a technical task and not as a part of enterprise cybersecurity management. The study offers a cross-disciplinary conceptual framework that combines Artificial Intelligence, Cybersecurity, Technology Management, Legal and Compliance considerations, and Digital Governance to offer a basis for future implementation and empirical validation in enterprise security settings.