AI-Driven Security Risk Mitigation: Enhancing Threat Assessment in Transit Infrastructure

Abstract The evolving threat landscape in transit infrastructure requires more adaptive, scalable, and precise security risk assessment and proposing proper risk mitigation measures. Traditional human-led approaches are often labor-intensive, subjective, and limited in their ability to incorporate real-time contextual data. This paper presents a novel AI-driven framework that combines geospatial analysis and large language models (LLMs) to automate the generation of structured, context-aware mitigation strategies aligned with industry standards and best practices. Our methodology involves a two-stage pipeline: (1) environmental feature extraction from Google Maps imagery and architectural design documentation, and (2) structured mitigation generation via controlled prompting of LLMs (GPT-3.5 and GPT-4). We evaluate model performance across 320 real-world threat scenarios spanning 32 threat types and 37 transit assets, using a multi-criteria rubric validated by security experts. Our results determine that GPT-4 model consistently outperforms GPT-3.5 in contextual relevance, logical consistency, and adherence to classification schemes, even though at higher computational cost. The framework also demonstrates high throughput, with practical implications for both rapid network-wide assessments and in-depth expert analysis. This study highlights the capacity of hybrid computer vision (CV)–LLM architectures in advancing autonomous security planning, while identifying key limitations and pathways for future improvement.