🤖 AI Summary
This study addresses the limitations of traditional risk matrices in supporting fine-grained, context-sensitive risk decision-making within complex dynamic systems. The authors propose a traceable, three-stage risk analysis framework: first, employing a multidimensional polar-coordinate heatmap to enable context-aware risk prioritization; second, constructing Bowtie causal barrier models for high-priority risks; and third, automatically transforming these Bowtie models into Bayesian networks to facilitate dynamic inference and “what-if” scenario analysis. A key innovation lies in explicitly modeling barriers as activated nodes, thereby establishing an integrated pathway from macro-level risk screening to micro-level intervention. Validation in a real-time payment gateway setting demonstrates that the proposed approach significantly enhances the transparency, auditability, and operational readiness of risk analysis.
📝 Abstract
Risk matrices (heatmaps) are widely used for information and cyber risk management and decision-making, yet they are often too coarse for today's resilience-driven organizational and system landscapes. Likelihood and impact (the two dimensions represented in a heatmap) can vary with operational conditions, third-party dependencies, and the effectiveness of technical and organizational controls. At the same time, organizations cannot afford to analyze and operationalize every identified risk with equal depth using more sophisticated methods, telemetry, and real-time decision logic. We therefore propose a traceable triage pipeline that connects broad, context-sensitive screening with selective deep-dive analysis of material risks.
The Hagenberg Risk Management Process presented in this paper integrates three steps: (i) context-aware prioritization using multidimensional polar heatmaps to compare risks across multiple operational states, (ii) Bowtie analysis for triaged risks to structure causes, consequences, and barriers, and (iii) an automated transformation of Bowties into directed acyclic graphs as the structural basis for Bayesian networks. A distinctive feature is the explicit representation of barriers as activation nodes in the resulting graph, making control points visible and preparing for later intervention and what-if analyses. The approach is demonstrated on an instant-payments gateway scenario in which a faulty production change under peak load leads to cascading degradation and transaction loss; DORA serves as the reference framework for resilience requirements. The result is an end-to-end, tool-supported workflow that improves transparency, auditability, and operational readiness from prioritization to monitoring-oriented models.