In today’s digital world, our financial systems are more than just banks, they are the invisible backbone of our daily lives. From tap-to-pay morning coffees to major business investments, millions of transactions happen every second. But as our banking becomes faster and more connected, the methods used by cyber-criminals have also evolved. To protect these vital systems, we need tools that can see more clearly and think faster than ever before.
As part of the European CyberAId initiative, our team at InQbit (IQB) is developing a new way to monitor these digital networks. We are combining two cutting-edge technologies eBPF and Graph Analytics to create a high-speed “radar” for financial fraud.
The Problem: Finding a Needle in a Digital Haystack
Traditional security systems often struggle with two major issues. First, they can be “heavy,” slowing down the very banking systems they are meant to protect. Second, they often look at transactions in isolation.
Imagine a bank’s security system as a guard at a gate. The guard might notice if one person tries to enter with a fake ID. However, if a hundred people enter through different gates over several hours, each doing something slightly unusual but seemingly harmless, the guard might miss the bigger picture. In the world of modern fraud like money laundering criminals use complex webs of small actions to hide their tracks.
eBPF: The High-Speed “Eyes” of the System
To solve the speed problem, we use a technology called eBPF (extended Berkeley Packet Filter). Think of eBPF as a series of tiny, ultra-fast sensors placed directly into the “nervous system” of a computer’s operating system.
Because these sensors live deep inside the system, they can watch every digital movement—network traffic, file changes, and data transfers—with almost zero delay. This gives us “super-vision” into core banking platforms without slowing down your transactions or requiring intrusive software updates.
Graph Analytics: Connecting the Dots
If eBPF provides the eyes to see the data, Graph Analytics provides the brain to understand it. Instead of looking at transactions as a simple list, graph analytics views them as a massive web of connections.
By mapping out how accounts, devices, and locations are linked, we can spot patterns that are invisible to regular software. For example, if a small amount of money moves through five different accounts in three countries and eventually ends up back where it started, our “graph” recognizes this as a classic sign of a sophisticated fraud ring. This approach helps us identify complex attacks while ignoring the normal “noise” of everyday banking, leading to fewer false alarms and faster responses.
Real-World Safety for Real People
This isn’t just a technical experiment, it’s about making the financial world safer for everyone. European regulations, like the Digital Operational Resilience Act (DORA), now require banks to detect and report major security incidents faster than ever often within just 24 hours.
By using our high-speed monitoring tools, we aim to go even further. Our goal is to provide notifications in under five minutes. This means that if a sophisticated attack begins, the system can spot the pattern and alert the right people before major damage is done.
A Smarter Defense
In the CyberAId project, these “eyes” and “brains” are also linked to advanced AI assistants. When our system detects a suspicious pattern, these AI agents can immediately suggest the best way to stop the threat, helping human security experts act with precision.
At InQbit, we believe that the best security should be invisible to the user but impossible for the criminal to bypass. By bringing together the speed of eBPF and the intelligence of Graph Analytics, we are building a more resilient financial future for Europe.
