An AI-driven cybersecurity tool designed to bridge the gap between complex vulnerability data and actionable IT operations.
Cybersecurity databases (like the NVD) output dense, jargon-heavy JSON files when reporting vulnerabilities. This project automates the retrieval of that data and uses a local Large Language Model (LLM) orchestrated by LangGraph to translate it into a clear, concise, and actionable threat report.
- Automated Data Retrieval: Fetches real-time CVE data from the NIST National Vulnerability Database (NVD) REST API.
- Intelligent Parsing: Extracts and sanitizes critical fields (Description, CVSS Severity Score, Published Date) from massive JSON payloads.
- Local AI Analysis: Utilizes a local LLM (via Ollama) to guarantee data privacy and zero API costs.
- Agentic Workflow: Built with LangGraph to orchestrate a state-machine that fetches, analyzes, and formats the final report.
- Language: Python 3.10+
- Data Source: NIST NVD API
- AI Orchestration: LangChain & LangGraph
- LLM Provider: Ollama (Local Execution)
- Environment Management:
venv,python-dotenv
(Installation and usage instructions will be added as the project progresses)