GraphMind AI

Graph Visualization
Hybrid Tool Calling
Architecture Diagram
Barcharts
LLM Generated AQL Queries

🚀 Inspiration

Cyber threats are evolving faster than ever, yet security teams struggle with outdated, manual processes that leave organizations vulnerable. With the exponential rise in CVEs, we saw an opportunity to leverage AI, knowledge graphs, and agentic automation to transform vulnerability intelligence from reactive to proactive and preventive defense.

🔍 What It Does

GraphMind AI is an MCP-powered cybersecurity intelligence system that:
✅ Ingests and models the CVE dataset as a graph in ArangoDB.
✅ Uses LangGraph agents to process natural language queries and dynamically generate AQL and NetworkX queries.
✅ Leverages NVIDIA cuGraph for centrality analysis and community detection, helping security teams prioritize vulnerabilities.
✅ Provides an interactive, real-time dashboard powered by MCP for visualizing and mitigating threats effectively.

🛠️ How We Built It

🔹 Data Ingestion & Graph Modeling: We structured the CVE dataset into ArangoDB, correcting inconsistencies in vendor-product mappings.
🔹 LangGraph & Multi-Agent Orchestration: We built LLM-powered agents to automate queries and responses.
🔹 GPU-Accelerated Graph Analytics: Integrated NVIDIA cuGraph to identify high-risk vulnerabilities efficiently.
🔹 MCP-Powered Visualization: Created an interactive dashboard that enables real-time exploration of threats.

🚧 Challenges We Ran Into

⚠️ Data inconsistencies: The hackathon dataset initially mapped all vendors to a single product—we had to curate and restructure it using CSVs.
⚠️ Optimizing multi-agent workflows: Balancing query complexity, performance, and accuracy required fine-tuning our approach.
⚠️ Integrating AI and graph analytics: Aligning LLM-generated queries with ArangoDB, LangGraph, and cuGraph was a technical challenge.

🏆 Accomplishments That We're Proud Of

✅ Successfully transformed raw CVE data into an intelligent, interactive cybersecurity system.
✅ Built a seamless integration between ArangoDB, LangGraph, and NVIDIA cuGraph for real-time graph analytics.
✅ Developed an MCP-powered dashboard that allows security teams to query, visualize, and mitigate vulnerabilities efficiently.

📚 What We Learned

📌 The power of graph-based security intelligence and agent-driven automation.
📌 How to optimize AI-driven query generation using multi-modal capabilities of ArangoDB LangGraph and MCP.
📌 How GPU-accelerated graph analytics can significantly enhance vulnerability prioritization.

🚀 What’s Next for GraphMind AI

🔹 Expanding beyond CVE data to include real-time threat feeds for live vulnerability tracking.
🔹 Enhancing AI-driven risk prediction by integrating machine learning models.
🔹 Building an enterprise-ready solution to help security teams automate vulnerability assessment and mitigation at scale.