This system automates compliance audits by embedding policies and regulatory documents into a vector store, using retrieval-augmented LLM queries to analyze and report compliance gaps. Users ingest their compliance corpus, configure the vector database, and ask tailored audit questions. The AI agent retrieves relevant sections and produces structured audit reports, highlighting potential violations, recommending actions, and logging each step for traceability.

Deep Threat Model leverages GPT-driven algorithms to analyze your application’s architecture and automatically generate comprehensive threat model diagrams. After specifying the application name and a brief description, the tool identifies assets, categorizes trust boundaries, and enumerates threat actors. It then maps potential attack vectors and suggests security controls, outputting the results in editable Mermaid or PlantUML notation. The AI engine ensures consistency across diagrams, flagging missing controls and highlighting high-risk components. Diagrams can be exported, refined manually, or integrated into CI/CD pipelines for continual security reviews. This streamlined approach reduces manual effort, minimizes errors in threat identification, and accelerates security documentation for development and compliance teams.