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

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.