threat-modeling.sgit.ai / papers / Graphs of Graphs of Graphs (G³) in Threat Modeling

Graphs of Graphs of Graphs (G³) in Threat Modeling

The reference architecture behind the mechanism: multi-view, multi-graph modelling, organic file-based evolution of the threat graph over time, and ontologies/taxonomies/standards linked in as semantic layers rather than baked into one schema.

Published

2025-05-30, docs.diniscruz.ai

Role

the architecture

Co-authorship

Several of these papers credit “Dinis Cruz and ChatGPT Deep Research” in their front matter — kept visible rather than smoothed away, per this site's attribution rule

Source

https://docs.diniscruz.ai/docs/2025/05/30/graphs-of-graphs-of-graphs-g3-in-threat-modeling.md — the paper itself, on docs.diniscruz.ai. This page summarises; it does not reproduce.

What it argues

Multi-view architecture: the same underlying graph read as a STRIDE view, an ATT&CK view, a business-impact view — without duplicating the data per view.
File-based organic evolution: the graph grows the way a codebase does, in diffable files, rather than as a single opaque database blob.
Ontology linking: STRIDE, MITRE ATT&CK, CAPEC, OWASP ASVS attach as layers over the graph, referenced and linked rather than reproduced.
A worked supply-chain case study, and a closing section on personalisation, traceability and automation as the benefits that follow from the architecture.

What checks it

This paper states a mechanism and a set of consequences. The ThreatModCon 2025 vault is the working proof at the scale this and the sibling papers argue for — not a demo built to illustrate the paper, but a published vault carrying real graph data (51 nodes, 179 threats across eleven linked layers). /validated/ is the sharper instance: a threat model checked, finding by finding, against the code it describes.

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