AI-Driven Cybersecurity Threats: A Survey of Emerging Risks and Defensive Strategies

TL;DR

This survey investigates AI-driven cybersecurity threats and the gaps in defense, mapping AI capabilities to threat modalities through a comparative taxonomy and synthesizing evidence from 70+ sources. It highlights four main AI-enabled threat families—deepfakes, adversarial attacks, automated malware, and AI-powered scams—and discusses current detection, defense, and regulatory strategies. Key contributions include proposing hybrid detection pipelines and benchmarking frameworks, and identifying gaps where explainable and regulatory-aligned AI defenses are needed. The work underscores the practical importance of interdisciplinary collaboration and policy engagement to maintain trust and security in digital ecosystems.

Abstract

Artificial Intelligence's dual-use nature is revolutionizing the cybersecurity landscape, introducing new threats across four main categories: deepfakes and synthetic media, adversarial AI attacks, automated malware, and AI-powered social engineering. This paper aims to analyze emerging risks, attack mechanisms, and defense shortcomings related to AI in cybersecurity. We introduce a comparative taxonomy connecting AI capabilities with threat modalities and defenses, review over 70 academic and industry references, and identify impactful opportunities for research, such as hybrid detection pipelines and benchmarking frameworks. The paper is structured thematically by threat type, with each section addressing technical context, real-world incidents, legal frameworks, and countermeasures. Our findings emphasize the urgency for explainable, interdisciplinary, and regulatory-compliant AI defense systems to maintain trust and security in digital ecosystems.

Figures (3)

• Figure 1: Different categories of deepfakes
• Figure 3: Global malware sample growth from 2010 to 2024
• Figure 4: Malware Detection based on Target, Features, Pre-processing, AI, and Dataset.