Webinar: Risk Modeling with Bayesian Networks – Beyond Bowties: A Cybersecurity Case Study

Live Webinar with Q&A Session on Thursday, September 25, 2025, at 11:00 a.m. (EDT, UTC-4)

Webinar Description

Risk management professionals have long relied on the Bowtie method for its intuitive, visual approach to mapping the pathways from causes to consequences. But what if you could supercharge this methodology? What if your risk model wasn’t just a static diagram, but a dynamic, intelligent system capable of probabilistic inference, quantitative analysis, and multi-directional “what-if” simulations?

This webinar will introduce you to the next evolution in risk analysis: Risk-Centric Causal Networks (RCCN). We’ll demonstrate how to extend the familiar Bowtie structure into a powerful Bayesian Network framework, enriching it with qualitative and quantitative data to create a living model of your risk environment.

What You’ll Learn

• How to move from static diagrams to living Bayesian models that support:
  • Forward prediction (simulation)
  • Backward diagnosis (root-cause analysis)
  • Sensitivity analysis to identify key risk drivers and leverage points
• The principles of Risk-Centric Causal Networks, including six key node types:
  • Triggers, Controls, Events, Main Risk, Consequences, and Mitigants
• How to integrate qualitative insights with quantitative structure for more robust, actionable models
• How to combine expert knowledge, regulatory guidance, and GenAI-generated content in a single risk framework

Case Study: Cybersecurity Risk in Critical Infrastructure

To showcase this approach, we’ll explore a real-world case: cyberattacks on critical infrastructure. While we are not cybersecurity experts, we will rely on Hellixia, our GenAI-powered Subject Matter Assistant, to:

• Rapidly explore the domain using HellixMap
• Build an initial risk model derived from GenAI-encoded knowledge
• Refine a second model based on the ANSSI Guide (“Recommandations pour la protection des systèmes d’information essentiels”)
• Validate and analyze the model using BayesiaLab, including scenario simulations and sensitivity analysis
• Finally, publish the validated model:
  • On HellixMap, for visual exploration and qualitative sharing of the model’s structure
  • On the WebSimulator, for real-time inference, scenario testing, and decision support through an interactive web-based application

Live Demonstrations

• Hellixia: AI-powered domain exploration & knowledge structuring
• HellixMap: Visual, collaborative causal modeling
• BayesiaLab: Quantitative Bayesian model building & inference
• WebSimulator: Publish and share interactive risk models instantly

Commercial Launch

This webinar also marks the official launch of:

• HellixMap – our next-generation platform for causal modeling and collaboration
• WebSimulator v2.0 – enhanced, user-friendly simulation deployment for stakeholders

Together with Hellixia and BayesiaLab, these tools form a comprehensive, GenAI-augmented ecosystem for modern, intelligent risk analysis.

Who Should Attend?

• Risk Managers & Analysts
• Cybersecurity & Resilience Professionals
• Data Scientists and Knowledge Engineers
• Compliance, Safety, and Audit Officers
• Anyone looking to enhance risk analysis with AI and Bayesian modeling

Reserve your spot today to explore how Bayesian networks and Generative AI can transform your approach to risk—from static diagrams to interactive, intelligent models.

About the Presenters

Lionel Jouffe

Dr. Lionel Jouffe is co-founder and CEO of France-based Bayesia S.A.S. Lionel holds a Ph.D. in Computer Science from the University of Rennes and has worked in Artificial Intelligence since the early 1990s. While working as a Professor/Researcher at ESIEA, Lionel started exploring the potential of Bayesian networks. After co-founding Bayesia in 2001, he and his team have been working full-time on the development of BayesiaLab. Since then, BayesiaLab has emerged as the leading software package for knowledge discovery, data mining, and knowledge modeling using Bayesian networks. It enjoys broad acceptance in academic communities, business, and industry.

Stefan Conrady

Stefan Conrady has over 20 years of experience in decision analysis, analytics, market research, and product strategy, having worked with Mercedes-Benz, BMW Group, Rolls-Royce Motor Cars, and Nissan across North America, Europe, and Asia. As Managing Partner of Bayesia USA and Bayesia Singapore, he is widely recognized as a thought leader in applying Bayesian networks to research, analytics, and decision-making. Together with his business partner, Dr. Lionel Jouffe, he co-authored Bayesian Networks & BayesiaLab — A Practical Introduction for Researchers, an influential resource now widely cited in academic literature. With their deep expertise in Bayesian networks for Key Driver Analysis and Optimization, Stefan and Lionel are highly sought-after consultants, advising global leaders such as Procter & Gamble, Coca-Cola, UnitedHealth Group, L’Oréal, the World Bank, and many of the world’s largest market research firms.