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@article{194420,
        author = {Pansul Saxena and Aryan Thakur and Mr. Saharsh Gera},
        title = {Generative Artificial Intelligence in Cybersecurity: A Systematic Review of Emerging Threats, Defensive Capabilities, and Future Research Directions},
        journal = {International Journal of Innovative Research in Technology},
        year = {2026},
        volume = {12},
        number = {10},
        pages = {3431-3439},
        issn = {2349-6002},
        url = {https://ijirt.org/article?manuscript=194420},
        abstract = {The recent mass deployment of Generative Artificial Intelligence (GenAI) technologies — including Large Language Models (LLMs) such as GPT-4 and Claude, and diffusion-based image synthesis models — has produced a dual-sided paradigm shift across the cybersecurity spectrum. On one hand, GenAI provides malicious actors with automation and scale for malicious campaigning, deepfake media, malicious code generation, and scale-based attacks on software vulnerabilities. Conversely, these same generative capabilities can be leveraged defensively to detect anomalies, automate penetration testing, produce threat intelligence, and respond to intrusions adaptively. This paper presents a systematic literature review of the intersection between GenAI and cybersecurity, analysing 142 peer-reviewed articles published between 2020 and 2024 indexed on Scopus and Web of Science. Following a Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) approach, we organise findings across six thematic areas: (1) adversarial threat generation, (2) social engineering amplification, (3) malware development and evasion, (4) AI-augmented intrusion detection, (5) vulnerability assessment and red-teaming, and (6) governance and ethical implications. We identify a considerable research imbalance, with offensive applications outnumbering defensive research at a ratio of 2.1:1. We determine essential research gaps, introduce a taxonomy of GenAI-driven cyber threats and defences, and outline a future research agenda. The findings carry significant implications for policymakers, practitioners, and scholars at the intersection of artificial intelligence and information security.},
        keywords = {Generative AI; Large Language Models; Cybersecurity; Deepfakes; Malware Generation; Phishing; Intrusion Detection; Adversarial Machine Learning; AI Governance},
        month = {March},
        }