This paper introduces the Safe-Cities Chatbot, a novel conversational AI system designed to support urban security planning and emergency management, with a focus on complex cross-border scenarios. The system integrates Retrieval-Augmented Generation (RAG) techniques with domain-specific knowledge from the Security Vulnerability Assessment (SVA) framework, enabling context-aware assistance for security professionals. Unlike existing solutions, the chatbot was developed and evaluated using a real-world test — Piazza Transalpina, a public square on the Italian-Slovenian border — incorporating local regulatory constraints, spatial dynamics, and multi-agency coordination challenges. The chatbot supports three core functions: guiding users through structured security assessments, offering event-specific planning advice, and simulating emergency scenarios tailored to the location. We detail the system’s architecture, its contextual reasoning strategies, and the simulation engine design. Our results demonstrate the potential of AI-driven conversational tools to enhance situational awareness, improve preparedness, and facilitate security planning across jurisdictions — a growing need in the face of new urban threats and regulatory requirements such as the NIS2 directive.

Talk Smart, Stay Safe: Intelligent Conversational Agents for Urban Security / Bianchini, Filippo; Van Den Heuvel, Willem-Jan; Marinacci, Matteo; Mecella, Massimo; Rossi, Jacopo; Tamburri, Damian A.. - 2602 CCIS:(2025), pp. 37-56. ( 19th Symposium and Summer School on Service-Oriented Computing, SummerSOC 2025 grc ) [10.1007/978-3-032-07313-6_3].

Talk Smart, Stay Safe: Intelligent Conversational Agents for Urban Security

Matteo Marinacci;Massimo Mecella;
2025

Abstract

This paper introduces the Safe-Cities Chatbot, a novel conversational AI system designed to support urban security planning and emergency management, with a focus on complex cross-border scenarios. The system integrates Retrieval-Augmented Generation (RAG) techniques with domain-specific knowledge from the Security Vulnerability Assessment (SVA) framework, enabling context-aware assistance for security professionals. Unlike existing solutions, the chatbot was developed and evaluated using a real-world test — Piazza Transalpina, a public square on the Italian-Slovenian border — incorporating local regulatory constraints, spatial dynamics, and multi-agency coordination challenges. The chatbot supports three core functions: guiding users through structured security assessments, offering event-specific planning advice, and simulating emergency scenarios tailored to the location. We detail the system’s architecture, its contextual reasoning strategies, and the simulation engine design. Our results demonstrate the potential of AI-driven conversational tools to enhance situational awareness, improve preparedness, and facilitate security planning across jurisdictions — a growing need in the face of new urban threats and regulatory requirements such as the NIS2 directive.
2025
19th Symposium and Summer School on Service-Oriented Computing, SummerSOC 2025
Conversational AI; Large Language Models (LLMs); Retrieval Augmented Generation (RAG); Security Vulnerability Assessment (SVA) Framework
04 Pubblicazione in atti di convegno::04b Atto di convegno in volume
Talk Smart, Stay Safe: Intelligent Conversational Agents for Urban Security / Bianchini, Filippo; Van Den Heuvel, Willem-Jan; Marinacci, Matteo; Mecella, Massimo; Rossi, Jacopo; Tamburri, Damian A.. - 2602 CCIS:(2025), pp. 37-56. ( 19th Symposium and Summer School on Service-Oriented Computing, SummerSOC 2025 grc ) [10.1007/978-3-032-07313-6_3].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11573/1755628
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