SentinelGraph

Title: Building Autonomous Cyber Incident Response using Model Context Protocol & FalkorDB

1. The Core Problem: Fragmented Context in Incident Response
   Security analysts and autonomous security tools struggle to correlate alerts across siloed infrastructure. When an Incident Response (IR) agent investigates an alert, retrieving contextual telemetry—such as open ports, compromised credentials, and active network connections—requires complex multi-hop queries that traditional relational or document databases process far too slowly.

2. The Solution: SentinelGraph
   SentinelGraph addresses this challenge by pairing FalkorDB's graph analytics engine with the Model Context Protocol (MCP). This architecture exposes live graph analytics directly to autonomous security agents as standardized tools.
   Through native OpenCypher queries and graph algorithms like PageRank and Betweenness Centrality, SentinelGraph allows AI agents to:
   -Instantly identify crown-jewel assets at risk during active lateral movement.
   -Traverse complex multi-hop attack graphs in under 2 milliseconds.
   -Execute autonomous containment actions via FastAPI backends and Next.js control dashboards.

By leveraging MCP, security agents can inspect infrastructure topologies, run attack graph traversals, and log forensic insights without exposing raw database credentials or sacrificing system boundaries.

1. Key Engineering Learnings -Graph Centrality Exposes Blast Radius: Graph algorithms like PageRank provide immediate quantitative scoring for asset criticality, allowing containment agents to prioritize isolation where it matters most. -Standardizing Agent Tools via MCP: Standardizing tool execution via Model Context Protocol drastically reduces agent integration friction, creating a clean abstraction layer between LLM reasoning and graph database operations.