This presentation explores the transformative potential of generative artificial intelligence—particularly large language models (LLMs)—in addressing critical challenges in domains such as cybersecurity, intelligent transportation systems (ITS) and political sciences. * Generative AI–Enhanced Threat Modeling in ITS: We develop an LLM-based framework to automate threat modeling for complex intelligent transportation systems by mapping information flows to MITRE ATT&CK techniques and NIST Cybersecurity Framework controls. The approach evaluates multiple AI methods, including zero-shot learning, RAG, multimodal reasoning, in-context learning, and fine-tuning. * Policy Analysis for Secure Transportation Systems: This project enhances transportation cybersecurity policy using AI-driven legal analysis and stakeholder engagement. Building on the TraCR AI system, it integrates U.S. and international regulations and uses agentic AI and graph-based retrieval to identify policy gaps and propose improvements for data security and privacy in autonomous transportation. * Conflict and Political Violence Monitoring: We developed ConfliBERT, a domain-specific pretrained language model for analyzing conflict and political violence data, which outperforms general-purpose LLMs in classification and question-answering tasks and has over 14,000 downloads on GitHub and Hugging Face. We also proposed ensemble-based active learning methods—Ensemble Union and Ensemble Intersection—that combine multiple heuristics to improve sample selection. Experiments on the United Nations Parallel Corpus show these approaches achieve performance comparable to full-dataset training while requiring far fewer labeled examples. * Cybersecurity Intelligence Extraction: In partnership with researchers at NIST, we automated the extraction of cyber attack techniques from Common Vulnerabilities and Exposures (CVE) and Cyber Threat Intelligence (CTI) reports. These extracted techniques are mapped to the MITRE ATT&CK framework using a combination of LLMs and active learning strategies. We have shown how this structured, machine-assisted analysis enhances the ability of security analysts to respond to emerging threats more effectively.