A Recent Example That Shows How This Works
Last November, we were monitoring transaction patterns for a client running a token bridge between Ethereum and a Layer 2 network. Around 3am on a Tuesday, our system flagged a series of deposits that looked almost normal—amounts were reasonable, timing wasn't suspicious, nothing that would trigger most standard alerts.
But the pattern of wallet interactions was slightly off. The addresses had minimal prior history, and they were all interacting with the bridge in a specific sequence that we'd seen before in a previous attack attempt on a different protocol.
We called the client's technical lead at 3:30am. By 4:15am, they'd paused the bridge contract. By 7am, we'd mapped out the full network of addresses involved. Turned out to be an attempted exploit that would have drained about $2.3 million if it had completed.
What made the difference wasn't fancy AI or complicated algorithms. It was recognizing a pattern because we'd seen something similar before, and having systems set up to catch subtle deviations from normal behavior. Plus having someone actually watching at 3am.
The client now has monitoring specifically tuned to catch that type of interaction pattern. And we've shared those learnings with other bridge operators we work with—in ways that don't compromise anyone's security specifics, obviously.
