Cyber threats are becoming increasingly complex, causingtraditional security systems to struggle in keeping up and highlightingthe need for advanced solutions. Large Language Models (LLMs), suchas OpenAI’s ChatGPT and Meta AI’s LLaMA, have shown great potentialto transform cybersecurity workflows with their abilities in naturallanguage understanding, pattern recognition, and automated reasoning.These models are particularly promising for tasks like networkmonitoring, threat detection, and security alert triage. However, challengesrelated to the reliability of outputs, adversarial risks, and ethicalconcerns must be addressed. This paper presents a comprehensive surveyof LLM-based approaches for security testing and evaluates threeopen-access LLMs, including Mistral-7B, Qwen3-8B, and Llama3.1-8B,demonstrating their ability to enhance security alert analysis. Our findingssuggest that LLMs can improve alert clarity and usability, makingthem more accessible to non-experts while providing valuable insightsfor developers.