From automation to CI/CD: a comparative evaluation of threat modeling tools

As threat modeling involves the architecture-centric identification, analysis and mitigation of security threats, it is considered an essential activity of the secure development lifecycle (SDLC). Threat modeling outcomes are used to guide the mitigation and risk management process, and to determine areas of focus in later development stages (e.g., testing and verification). Traditional threat modeling methods and techniques are driven by human experts and stakeholders and mainly conducted manually. Recently, focus has shifted towards automating (parts of) the process through improved tool support.In this paper, we evaluate the extent to which this trend also leads to easier development pipeline integration of threat modeling tools, i.e. whether this also enables conducting threat modelcentric analysis within continuous integration and development (CI/CD) build pipelines so that they can contribute to the overall development-centric quality assurance process (SecDevOps). We first articulate the key requirements for threat modeling tools to be compatible or suited for CI/CD, and then we systematically evaluate and compare seven automated threat modeling tools against these requirements. Finally, we provide recommendations towards making threat analysis tools and enablers both more suitable for integration in a CI/CD context and hence, more compatible to modern software engineering processes.

Authors

Institutions

Publication Details

Published
2024-10-07
DOI
https://doi.org/10.1109/secdev61143.2024.00010
Citations
5
Primary Topic
Network Security and Intrusion Detection
Type
article
Field-Weighted Citation Impact
1.74

Funders

Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

From automation to CI/CD: a comparative evaluation of threat modeling tools

Dimitri Van Landuyt, Laurens Sion, Wouter Joosen, Walewein Philips
5 citations
Network Security and Intrusion Detection
1.74
article

From automation to CI/CD: a comparative evaluation of threat modeling tools

Dimitri Van Landuyt, Laurens Sion, Wouter Joosen, Walewein Philips
article en
5 citations

Abstract

As threat modeling involves the architecture-centric identification, analysis and mitigation of security threats, it is considered an essential activity of the secure development lifecycle (SDLC). Threat modeling outcomes are used to guide the mitigation and risk management process, and to determine areas of focus in later development stages (e.g., testing and verification). Traditional threat modeling methods and techniques are driven by human experts and stakeholders and mainly conducted manually. Recently, focus has shifted towards automating (parts of) the process through improved tool support.In this paper, we evaluate the extent to which this trend also leads to easier development pipeline integration of threat modeling tools, i.e. whether this also enables conducting threat modelcentric analysis within continuous integration and development (CI/CD) build pipelines so that they can contribute to the overall development-centric quality assurance process (SecDevOps). We first articulate the key requirements for threat modeling tools to be compatible or suited for CI/CD, and then we systematically evaluate and compare seven automated threat modeling tools against these requirements. Finally, we provide recommendations towards making threat analysis tools and enablers both more suitable for integration in a CI/CD context and hence, more compatible to modern software engineering processes.

KU Leuven (BE)
KU Leuven, Vlaamse regering
Openalex Percentile: Top 14%
Network Security and Intrusion Detection
1.74
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.