Investigative Study of Role of Large Language Models in Cybersecurity

Abstract

This study presents an investigative study examining the dual role of Large Language Models (LLMs) in cybersecurity, analyzing both their defensive capabilities and associated risks. The research systematically explores how LLMs enhance threat detection through anomaly analysis, improve vulnerability assessment and code security, streamline incident response and forensics, and automate cyber threat intelligence extraction. Through comprehensive analysis, we demonstrate that LLMs excel at processing unstructured data, identifying behavioral anomalies, analyzing obfuscated code, reconstructing attack timelines, and extracting indicators of compromise from diverse intelligence sources. However, our investigation also identifies critical challenges, including adversarial misuse for generating sophisticated phishing campaigns and malicious code, hallucination-induced false positives that compromise operational reliability, and significant data privacy and model security concerns. The study evaluates implementation challenges across real-world cybersecurity datasets, including CIC-IDS 2017, UNSW-NB15, and PhishTank, providing practical insights into deployment considerations. We propose future directions emphasizing privacy-preserving architectures, multimodal capabilities for holistic threat analysis, explainable AI mechanisms for transparency, and robust governance frameworks. This research contributes a balanced perspective on LLM integration in cybersecurity, highlighting that responsible deployment requires carefully balancing innovation with security, accountability, and ethical considerations to maximize defensive benefits while minimizing exploitation risks.