Why honeypots don’t cry wolf

Most security tools watch real systems and try to decide whether what they see is bad. That is a hard problem: administrators run scripts at odd hours, backup jobs copy everything, developers do strange things with SSH. Every rule that catches an attacker also catches someone doing their job. The result is alert fatigue, and real intrusions that hide in the noise.

A decoy doesn’t have to guess

A honeypot flips the problem around. It is a fake system (a NAS, a printer, a file server) with no real users and no real purpose. Nobody is supposed to log in to it, scan it or query it. So when something does, there is nothing to decide: that is the signal.

That is why decoys have such a low false-positive rate. The only “legitimate” traffic is your own monitoring and vulnerability scanners, and you know their addresses, so they go on an ignore list.

The catch: someone has to understand the alert

A honeypot event is usually raw protocol detail: an SMB negotiate, an NTLM username, a Redis command. Most teams have no one awake at 2 a.m. who reads those fluently. That is the gap Kandaro fills: every event is explained in plain English, and related events become one incident that says what happened and what to do next.

Attackers aren’t only human any more

Autonomous AI agents now explore networks too. They read every file they find and talk to anything that looks helpful, which makes them very good at walking into decoys. Read what an AI agent does when it lands in your network.

Want to try it? Deploy your first decoy in about a minute, or read more about Kandaro Deception.