Risk teams now have a new way to build fraud rules.
Describe the problem in plain language, and AI Rule Builder handles the rest—finding the right signals from Incognia's library of 500+ and generating the complete rule logic, ready to review.
Incognia's signal library didn't reach 500+ by accident.
Fraud is complex. Catching it with precision means having the right signal for the right pattern—and enough of them to cover the combinations that separate real users from sophisticated attackers. That's a genuine advantage.
Using a library that large is a skilled process.
Experienced analysts navigate it methodically—finding the right signal, understanding its context, copying the syntax, wiring up the reason codes. Each step requires judgment and familiarity with how the signals work together.
That's the nature of working with a tool built for precision at this scale.
That’s the core idea behind AI Rule Builder.
Instead of navigating the library to find and assemble the right pieces, you type what you're trying to stop in plain language.
Something like: "Block devices associated with account takeover attempts."
The engine reads that intent, maps it to the right signals from across the library, and builds the complete rule block—including reason codes—ready for your review.
The output isn't a list of signals to explore. It's the finished logic, in the same policy structure analysts already work with. Same format, same controls, generated from intent instead of assembled manually.
This is where AI Rule Builder is fundamentally different from a general-purpose AI assistant attached to a rule editor.
The engine is trained specifically on Incognia's proprietary signal library, policy connectors, and reason code structure.
It doesn't match keywords to rule names—it tokenizes the library semantically, understanding what signals mean and how they interact, not just what they're called.
Think about the evolution from a paper map to a GPS. The map works—it's precise, and it gets you there. The GPS just removes the work of plotting the route yourself. You describe where you need to go, and it figures out the path.
AI Rule Builder works the same way. Describe the threat, and the engine finds the signals.
That distinction matters when the problem is multi-dimensional.
A query like "flag devices that have switched accounts multiple times from the same location cluster" requires reasoning across device reputation, location history, and behavioral timing simultaneously. Keyword search surfaces candidates. AI Rule Builder synthesizes the logic.
And before anything goes live, Shadow Mode lets teams test generated rules against real traffic without affecting live decisions.
AI Rule Builder frees senior analysts to spend more time on the threat patterns that need their attention and less on signal lookup and syntax.
But the impact goes beyond individual efficiency.
Deploying sophisticated logic has always required a familiarity with the library—knowing which signals to reach for and how they interact. That expertise takes time to develop, and newer team members build it progressively as they work with the platform.
AI Rule Builder accelerates that process.
Someone who can describe a threat accurately can now deploy logic that reflects it on day one, increasing your speed to value.
Rule building has always evolved alongside the threats it's designed to stop.
AI Rule Builder is the next step in that evolution—moving from manually assembling the right signals to simply describing the outcome you need.
Trained on Incognia's own policy language and proprietary signal library, the engine understands the library from the inside out. That foundation is what makes intent-based rule building possible at this level of precision.
AI Rule Builder opens to a select group of partners in Q2 beta, with general availability to follow.