Harnessing LLMs for Cyber-Resilience: A Systematic Survey in Threat Mitigation and Future Trajectories

By enabling contextual reasoning, adaptive defense mechanisms, and intelligent threat detection, the quick development of LLMs has drastically changed the cybersecurity landscape. LLMs provide a single framework that can comprehend complex cyber threats across disparate data sources, in contrast to standard machine learning approaches that mostly rely on handcrafted features and task-specific models. With a focus on domain-specific fine-tuning techniques and web content protection, this research methodically investigates how LLMs are used to promote AI-driven cybersecurity. A structured keyword selection and screening process was carried out in accordance with PRISMA criteria, yielding a curated collection of representative phrases that directed the literature investigation. For cybersecurity activities like phishing and spam detection, cyber threat intelligence, intrusion detection, malicious URL identification, DDoS mitigation, and IoT security, the report summarizes recent research on optimized LLMs. In order to adapt general-purpose LLMs to security-sensitive contexts, special attention is paid to parameter-efficient fine-tuning techniques, continuous pretraining, and instruction tweaking. Additionally, this research demonstrates how LLMs improve the security of web content by using contextual knowledge and reasoning to detect emerging dangers and social engineering assaults that circumvent conventional rule-based systems. This paper offers a succinct of the state of LLM-driven cybersecurity research by classifying the literature into logical subject areas and using graph-based representations to visualize keyword correlations. The review seeks to assist researchers and practitioners in comprehending current approaches, pointing out constraints, and spotting new trends in the safe and efficient use of LLMs for cybersecurity operations.