Personalization to Protection: A Review of AI-Based Cyber Defense Mechanisms in Marketing Systems

The rapid adoption of artificial intelligence (AI) in digital marketing has transformed how firms personalize customer engagement, optimize campaigns, and generate market intelligence. However, the same data-driven architectures that enable advanced personalization have also expanded the cybersecurity attack surface of modern marketing systems. As marketing platforms increasingly rely on automated data collection, real-time analytics, and AI-enabled decision-making, they have become attractive targets for cyber threats such as data breaches, ad fraud, model manipulation, and identity theft. This review examines the evolving role of AI-based cyber defense mechanisms in protecting marketing systems, shifting the focus from value creation through personalization to value preservation through protection. Drawing on peer-reviewed literature across marketing analytics, cybersecurity, information systems, and applied machine learning, the review synthesizes existing research on AI-driven security solutions deployed within marketing environments. Specifically, it examines machine learning and deep learning approaches used for intrusion detection, anomaly detection, fraud prevention, bot and click-fraud identification, customer identity protection, and secure data governance. The review categorizes these techniques into supervised, unsupervised, deep learning, and hybrid defense frameworks, highlighting their application across customer relationship management systems, programmatic advertising platforms, social media marketing, and e-commerce ecosystems. The findings indicate that AI-based cyber defense mechanisms consistently outperform traditional rule-based security approaches in detecting complex, evolving threats within marketing systems. However, their effectiveness is constrained by challenges related to data quality, algorithmic transparency, integration with legacy marketing technologies, and organizational cybersecurity maturity. Furthermore, the literature reveals a dominant emphasis on technical detection accuracy, with limited consideration of managerial interpretability, regulatory compliance, and consumer trust. This review contributes by developing a conceptual framework that links AI-enabled marketing functionalities, cyber threat vectors, and defensive AI capabilities to organizational outcomes such as brand trust, data integrity, and sustainable marketing performance. The paper concludes by outlining future research directions emphasizing explainable AI, privacy-preserving security models, ethical governance, and scalable cyber defense solutions tailored to marketing-driven digital ecosystems.