SentinelGraph: Building a Graph-Powered AI Cybersecurity Investigation Agent with FalkorDB
SentinelGraph: Building a Graph-Powered AI Cybersecurity Investigation Agent with FalkorDB How connected data can help security analysts investigate alerts, trace evidence, and make more explainable decisions. The Problem: Security Alerts Don’t Exist in Isolation Cybersecurity teams often deal with multiple alerts originating from different systems. Looking at each alert independently can make it…
Cybersecurity teams routinely face multiple security alerts from various systems. Investigating each alert in isolation can be challenging, as it may be hard to identify relationships, grasp the bigger picture, or determine if several signals constitute the same incident. To address this issue, I developed SentinelGraph, an AI-powered cybersecurity investigation agent that assists security analysts in exploring connections between alerts and comprehending the evidence behind an investigation.
Introduced as a graph-powered cybersecurity investigation system, SentinelGraph leverages FalkorDB, a graph database, to represent security alerts as interconnected data. This approach enables investigators to:
1. Explore relationships between seemingly unrelated security alerts
2. Traverse multi-hop connections to uncover relevant context
3. Trace evidence paths to explain why alerts may be related
4. Retrieve historical incident memory for additional context
5. Understand why certain candidates are grouped or excluded
6. Review recommended response actions before approving or rejecting them
The goal is not to replace security analysts but to provide them with a clearer understanding of the evidence, enabling more informed decision-making. SentinelGraph's workflow combines graph exploration, evidence analysis, historical context, and human review to offer a comprehensive investigation experience.
The architecture of SentinelGraph consists of a graph database (FalkorDB), an API backend (FastAPI), a web interface (Next.js and React), and a graph visualization tool (React Flow). The investigation process begins with security alerts, which are then explored for connected relationships using FalkorDB. The system exposes evidence paths and historical incident memory to provide context, while also explaining grouping and exclusion decisions.
Finally, the system recommends response actions, which are subject to human review before implementation.
Written by urgent.news from Dev.to's reporting — not their text. Machine-written — may contain errors; check the original before relying on it.
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