Webinar: Modeling a Multi-Dimensional Threat Landscape
October 16, 2025, 11:00 a.m. (EDT, UTC-4)
Webinar Description
Following the success of our first webinar on Risk-Centric Causal Networks (RCCNs) for cybersecurity risk, this new webinar continues the journey with a strategic, geopolitical use case: Switzerland’s 2024 national threat landscape, as identified by the Swiss Federal Intelligence Service (SRC). This time, we’ll start with the official threat radar and expand it into a structured causal model that captures systemic risks and their interactions.
The overall webinar objective is to show you how RCCNs help synthesize complex multi-threat environments into coherent, analyzable models that support both strategic insight and operational decisions.
What You’ll Learn
- How to turn a visual threat map into a Risk-Centric Causal Network
- How to extract actionable knowledge from a real-world intelligence report, i.e., the 2024 Situation Report of the Federal Intelligence Service of Switzerland
- How to restructure the generated model to reduce complexity and improve interpretability
- How to use sensitivity analysis, optimization policies, and Most Relevant Explanations (MREs) to support decision-making
Case Study: Building Risk-Centric Causal Networks
We’ll build two different models of the same core risk: the degradation of Swiss national security.
- From the threat radar: identifying hotspots, themes, and early warning signs
- From the full intelligence report: using Hellixia to generate causal structures directly from the document
Live Demonstration of Tools
- Hellixia – GenAI assistant for domain discovery and structure generation
- HellixMap – Visual and collaborative modeling of qualitative risk networks
- BayesiaLab – Quantitative modeling, sensitivity analysis, and probabilistic scenario simulation
- WebSimulator – Interactive risk application for inference and stakeholder engagement
Who Should Attend?
- Risk Managers & Analysts
- Intelligence Analysts
- Strategic Planners
- Supply Chain Managers
- Insurance and Reinsurance Risk Underwriters
- Actuaries
- Policy Analysts
- Crisis and Emergency Management Officers
- J-2, G-2, and S-2 Officers
- Counterterrorism Analysts
- Wargaming Analysts
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.