How Knowledge Graphs and GraphRAG Make AI Deliver for Cybersecurity

This guest blog was contributed by Dominik Tomicevic, CEO of knowledge graph leader Memgraph

Cybersecurity teams today face a massive challenge: making sense of thousands of alerts, logs, and signals — quickly. For CISOs, it’s essential to understand whether an alert is part of a broader attack, how far it’s spread, and what might be at risk.

Many now see GenAI and large language models (LLMs), with their capacity for complex reasoning and natural language understanding, as a critical part of the solution. But on their own, even advanced LLMs struggle to meet the precision and contextual depth cybersecurity demands.

Why? Because traditional LLMs are (a) limited in the amount of data they can process at once, and (b) not built to understand the relationships between users, devices, and behaviors. In domains like marketing, we can accept that LLMs generate plausible-sounding text without deep system understanding. In cybersecurity, that’s not just a limitation, it’s a potential risk to the entire organization.

So how do we make LLMs truly useful in cybersecurity? The key is connecting them to what they lack: structure—specifically, the relationship-rich architecture of graph databases. Graphs model the world as nodes (e.g., users, devices, IPs) and edges (the relationships between them), which is exactly how real-world attacks unfold: through lateral movement, suspicious behavior, or unusual access patterns.

This structure aligns naturally with how security teams think and investigate. It enables them to trace meaningful patterns and uncover deeper threats. But for LLMs to leverage this graph-based context, there must be a way to bridge the two worlds, transforming structured, connected data into something an LLM can reason over in real time.

Enter the GraphRAG dragon

GraphRAG (Graph-based Retrieval-Augmented Generation) bridges the gap between LLMs and real-time security data. It enables LLMs to reason over live graph structures by injecting structured, contextual information directly into prompts. The result: LLMs shift from generic chatbots to informed, capable security assistants.

Here’s how it works: you model your security data as a graph. Then, when an analyst asks a question, like “Has this user interacted with any flagged IPs in the last 48 hours?” GraphRAG translates it into a graph query, searches the data, retrieves the relevant information, and feeds it back to the LLM. The model then generates a clear, contextual response.

The outcome? Fast, accurate insights grounded in real data and no need for analysts to write Cypher or understand graph theory. They can simply ask questions in plain English and receive actionable answers or summaries like: “This alert was triggered by a phishing campaign leading to unauthorized access.”

Looking ahead, the next evolution is already underway: AI-powered cyber agents fueled by graph data. These agents will detect fraud patterns, trace privilege escalation paths, and explain suspicious activity — all in real time. Some of the banks we work with are already piloting this approach, using GraphRAG in combination with advanced graph algorithms to build adaptive fraud detection systems that evolve with the threat landscape.

The takeaway is clear: LLMs alone aren’t enough to tackle today’s cybersecurity challenges. But when paired with the structure and context of graph data through GraphRAG, we begin to unlock the real potential of AI in this space.

The future of AI in cybersecurity won’t be defined by bigger models, but by smarter integrations; and at the core of those, the evidence increasingly points to graphs and GraphRAG.