A curated collection of threat modeling resources, including methodologies, tools, books, and conference talks.
Awesome Threat Modeling is a curated GitHub repository that collects and organizes resources related to threat modeling and risk management. It provides links to methodologies like STRIDE and DREAD, tools for creating data flow diagrams, books, conference talks, and practical guides to help teams identify and mitigate security risks early in the development lifecycle.
Security professionals, software developers, system architects, and DevOps/DevSecOps teams who need to implement or enhance threat modeling practices within their projects or organizations.
It saves time by aggregating scattered, high-quality threat modeling resources into a single, structured list, making it easier for teams to learn best practices, choose appropriate tools, and stay updated with the latest advancements in the field.
a curated list of useful threat modeling resources
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Aggregates high-quality links to methodologies (e.g., STRIDE, DREAD), tools, books, and talks from trusted sources like OWASP and Microsoft, saving significant research time.
Organizes resources into clear sections such as Data Flow Diagrams and Threat Enumeration, making it easy to navigate and find relevant information quickly.
Encourages contributions from the security community, helping keep the list current with new tools like pytm and conference talks from recent events.
Includes a variety of tools from open-source options like OWASP Threat Dragon to commercial ones like IriusRisk, providing choices for different workflow needs.
It's merely a collection of external links; users must manually access, evaluate, and integrate each resource, which can be time-consuming and inefficient for immediate application.
Relies on community contributions without formal vetting, so some links might be outdated, broken, or of inconsistent quality, as noted by the reliance on external sources.
Doesn't offer curated learning paths or step-by-step instructions for beginners, assuming users have prior knowledge or will independently explore the disparate resources.