Abstract

The Cybersecurity Risk by Design (CRD) model represents a transformative enhancement to threat modeling practices within cybersecurity engineering. Integrating structured methodologies from traditional engineering disciplines—specifically, the Design Risk Model (DRM) and the Design Threat Model (DTM) —CRD addresses fundamental shortcomings that have historically limited the effectiveness of threat modeling. Current cybersecurity breaches highlight persistent gaps, which are exacerbated by the rapidly evolving complexities of advancements such as artificial intelligence (AI) and cloud infrastructures. Drawing upon cross-disciplinary expertise from civil, aerospace, and systems engineering, the CRD model proactively identifies, analyzes, and mitigates threats systematically from the earliest stages of design. Aligned closely with the mandatory threat modeling guidelines advocated by the National Institute of Standards and Technology (NIST), this approach provides cybersecurity engineers with a structured, defensible framework to dramatically enhance the accuracy and efficacy of threat modeling processes. Empirical evidence underscores substantial benefits, including significantly reduced vulnerabilities, improved cost efficiency, and strengthened operational resilience. Further augmented by intelligent engineering, which continuously integrates real-time threat intelligence and advanced analytics, the CRD model equips organizations with predictive capabilities and adaptive resilience, critical for securely managing contemporary, complex technological environments.

Key words: Cybersecurity Risk by Design (CRD), Design Risk Model (DRM), Design Threat Model (DTM), Intelligent Engineering, Threat Modeling (TM), Cybersecurity Engineering, Proactive Risk Mitigation, Artificial Intelligence (AI), Cloud Security, Operational Resilience, Cross-Disciplinary Engineering.