An AI-Powered Privacy Threat Modeling tool based on the LINDDUN framework by leveraging Large Language Models.
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Updated
Aug 6, 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
A Design skill to develop privacy first designs starting with the ontology
Security and privacy threat model for a conceptual healthcare SIEM using STRIDE, LINDDUN, DREAD and data-flow diagrams.
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.
Threat modeling, code, cloud and pipeline scanning, shadow-AI discovery, compliance checks and fixes, from your assistant. Remote MCP server.
Seven threat-modeling worksheets (STRIDE, LINDDUN, PASTA, attack tree, DFD, trust boundary, abuse case) plus five worked examples (web, mobile, ML, IaC, IoT).
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