An AI-Powered Privacy Threat Modeling tool based on the LINDDUN framework by leveraging Large Language Models.
-
Updated
Apr 26, 2026 - Python
An AI-Powered Privacy Threat Modeling tool based on the LINDDUN framework by leveraging Large Language Models.
This repository contains an example application for a Patient Community, inspired by a LINDDUN example privacy analysis.
A skill to analyze existing code against PbD principles
This project presents a Privacy-Aware Smart Healthcare Platform that implements real-time privacy threat modeling using the LINDDUN Privacy Threat Modeling Framework. The system dynamically analyzes how healthcare data flows through the application and identifies privacy risks associated with personal information.
Seven threat-modeling worksheets (STRIDE, LINDDUN, PASTA, attack tree, DFD, trust boundary, abuse case) plus five worked examples (web, mobile, ML, IaC, IoT).
Threat modeling, code, cloud and pipeline scanning, shadow-AI discovery, compliance checks and fixes, from your assistant. Remote MCP server.
Security and privacy threat model for a conceptual healthcare SIEM using STRIDE, LINDDUN, DREAD and data-flow diagrams.
A Design skill to develop designs ontology first
Add a description, image, and links to the linddun topic page so that developers can more easily learn about it.
To associate your repository with the linddun topic, visit your repo's landing page and select "manage topics."