Generative AI for Simulation-Based Risk Assessment in Critical Infrastructure
Keywords:
Generative artificial intelligence; critical infrastructure; simulation-based risk assessment; digital twins; system resilience; socio-technical systems; risk governance; uncertainty modelingAbstract
Critical infrastructure systems such as power grids, water distribution networks, transportation corridors, and communication systems are increasingly exposed to compound risks arising from climate variability, cyber-physical interdependencies, and socio-technical complexity. Traditional simulation-based risk assessment methods, while effective in modeling deterministic or probabilistic scenarios, are often constrained by rigid modeling assumptions, limited scenario diversity, and high computational overhead. Recent advances in generative artificial intelligence provide new opportunities for augmenting simulation environments with adaptive scenario generation, synthetic data creation, and dynamic stress testing capabilities. This paper examines the integration of generative AI within simulation-based risk assessment frameworks for critical infrastructure systems. It develops a systems-level perspective that situates generative models as intermediate reasoning and synthesis layers between raw infrastructure data and high-fidelity simulation engines. The analysis explores architectural patterns for coupling generative AI with agent-based models, digital twins, and Monte Carlo simulation frameworks, emphasizing issues of robustness, uncertainty propagation, and interpretability. It further investigates governance challenges, including validation of synthetic scenarios, accountability in AI-assisted decision-making, and the alignment of generative outputs with safety-critical standards. Through cross-domain conceptual case illustrations spanning energy systems, urban transportation, and water infrastructure, the paper demonstrates how generative AI can expand the envelope of risk exploration beyond historically observed events. The study concludes that while generative AI significantly enhances the expressive capacity of simulation-based risk assessment, it simultaneously introduces new layers of epistemic uncertainty that must be carefully managed through hybrid modeling architectures and human-in-the-loop governance structures. The paper provides a forward-looking perspective on scalable, resilient, and ethically grounded deployment pathways for generative AI in critical infrastructure risk analysis.
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