Satellite-based cloud computing cybersecurity threats have long posed significant challenges, particularly for satellite-based cloud infrastructure operators. While prior research has partially addressed these issues by mitigating threats and enhancing human response efficiency, this paper proposes a novel AI-Driven Threat Analysis and Response (TAR) framework. The study progresses in three main phases: (1) redefining urgent threats through a novel formula; (2) implementing a triage and analysis framework using augmented Large Language Models (LLMs); and (3) automating incident response via a Security Orchestration, Automation, and Response (SOAR) platform. Our prototype, tested in a simulated public cloud environments using real production threats, demonstrated a 17% improvement in handling low- and medium-urgency threats. Experimental results show our approach achieves 97.8% coverage in automatic threat classification, significantly outperforming traditional manual methods, which achieve 77.8% coverage. With high recall and precision in managing low- and medium-urgency threats, our method enhances manual efficiency through SOAR-enabled automation. Furthermore, our augmented method surpasses the state-of-the-art GPT-4 Turbo model in addressing security threats containing Chinese characters.