Generative Artificial Intelligence and Language Models in Cybersecurity: Opportunities, Threats, and Challenges

In recent years, Large Language Models (LLMs) have emerged as one of the most transformative technologies in artificial intelligence, playing a significant role in advancing cybersecurity systems. Owing to their capability to understand, interpret, and analyze natural language, these models facilitate the processing of unstructured data, including security logs, incident reports, and textual communications, thereby enhancing various cybersecurity tasks such as intrusion detection, malware analysis, phishing attack identification, and the automation of Security Operations Center (SOC) activities. Despite these advantages, the adoption of LLMs introduces several critical challenges and security concerns, including model hallucinations, susceptibility to prompt injection attacks, data poisoning, and privacy-related risks. This paper presents a comprehensive review of the applications of Large Language Models in cybersecurity, examining their major use cases while critically analyzing the associated threats, limitations, and vulnerabilities. Furthermore, the technical and operational challenges of deploying LLM-based cybersecurity solutions are discussed, and future research directions are outlined to support the development of more secure, trustworthy, and explainable intelligent systems. The findings indicate that LLMs are reshaping the cybersecurity landscape by shifting traditional rule-based approaches toward semantic-driven and intelligent threat analysis frameworks.

کلیدواژه ها:

Generative Artificial Intelligence ، Large Language Models ، Cybersecurity ، Cyber Threat Analysis ، Information Security