AI-Driven Cybersecurity Threats: A Survey of Emerging Risks and Defensive Strategies
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arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2026
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| _version_ | 1866917187665526784 |
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| author | Erukude, Sai Teja Marella, Viswa Chaitanya Veluru, Suhasnadh Reddy |
| author_facet | Erukude, Sai Teja Marella, Viswa Chaitanya Veluru, Suhasnadh Reddy |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2601_03304 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | AI-Driven Cybersecurity Threats: A Survey of Emerging Risks and Defensive Strategies Erukude, Sai Teja Marella, Viswa Chaitanya Veluru, Suhasnadh Reddy Cryptography and Security Artificial Intelligence 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. |
| title | AI-Driven Cybersecurity Threats: A Survey of Emerging Risks and Defensive Strategies |
| topic | Cryptography and Security Artificial Intelligence |
| url | https://arxiv.org/abs/2601.03304 |