Mitigating Cybersecurity Risks in LAN Environments Using AI Techniques

Abstract

Local Area Networks (LANs) are critical to organizational infrastructure, yet they remain highly vulnerable to sophisticated cyber threats such as insider misuse, ARP spoofing, privilege escalation, and zero-day exploits. Traditional defenses including firewalls, signature-based intrusion detection systems, and static authentication protocols are increasingly inadequate for dynamic and evolving attack vectors. Artificial Intelligence (AI) and Large Language Models (LLMs) offer new opportunities to strengthen LAN security through anomaly detection, contextual analysis, and adaptive response. This paper explores how machine learning (ML), deep learning (DL), and LLM-powered systems (e.g., ChatGPT, Gemini, Claude, Copilot, Falcon) can mitigate cybersecurity risks in LAN environments. It also examines the integration of AI with advanced authentication technologies such as Multi-Factor Authentication (MFA), biometric verification, behavioral biometrics, adaptive access control, and Zero Trust Architecture (ZTA). A hybrid framework is proposed that combines anomaly detection and mitigation of network security risks, ‘LLM assisted decision making, and AI enhanced authentication to detect and mitigate against security risks.