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

TL;DR

This survey examines emerging AI-driven cybersecurity threats, analyzes attack mechanisms and defense gaps, and proposes research opportunities for developing explainable, interdisciplinary, and regulatory-compliant AI security solutions.

Contribution

It introduces a comprehensive taxonomy linking AI capabilities with threat types and defenses, reviewing extensive literature and identifying key research directions.

Findings

AI threats include deepfakes, adversarial attacks, malware, and social engineering.

Current defenses are insufficient and need explainability and regulation.

Research opportunities include hybrid detection and benchmarking frameworks.

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.