Beyond the rulebook: mitigating enterprise risk in generative AI
PolyAI builds safety into the core of its AI — not just a rulebook — to protect enterprises from risk at every layer.
In early 2024, a chatbot for a car dealership made headlines for selling a Chevy Tahoe for just $1. It was a clear sign of a rulebook failure. When an AI is guided only by a list of written instructions, it is too easy for people to trick it. For big businesses that handle refunds or private data, these weak spots can lead to real financial hits and a damaged brand.
At PolyAI, we’ve moved past simple lists of do’s and don’ts. We build safety right into the bones of our system. By weaving security into the PolyAI SLU (Spoken Language Understanding) Engine , we keep the conversation safe from start to finish.
Multi-layered way to stay safe
Building a secure AI for your business takes two things: a model that is tough enough to handle tricks, and a setup that keeps it within strict bounds. We build in several layers of protection:
- Understanding every word: Most systems only look at text, but we are moving toward a speech-to-speech (S2S) future. While we currently use specialized models to sanitize telephony audio, our research is already pivoting toward Raven Omni —a model designed to understand the raw sound of a caller’s voice. This will allow us to cut through background noise and pick up on intent more accurately than traditional ASR ever could.
- Sticking to the facts: We make sure the AI only uses your company’s trusted data. Instead of just hiding facts that don't quite fit, we train Raven to handle distractions. We actually train the model with messier data than it will see in the real world, so it learns how to stay focused even when a caller tries to lead it astray.
- Hardening the model: Raven 3.5 is built from the ground up to spot and stop jailbreak attempts. We put our models through the wringer—testing them against long-winded crescendo attacks and hidden codes—to make sure the AI listens to its core training over a pushy user.
- Strict guardrails on every answer: We keep a close eye on what the AI says as it says it. If it tries to say something that breaks the rules, our system catches it and swaps it out before the caller ever hears it.
Fast, not frustrating
Safety usually slows things down, leading to that robotic pause while a bot double-checks its work. We’ve fixed this by running our safety checks at the same time the model is thinking. By checking what the user wants while the AI builds its answer, we keep things safe without losing the split-second speed people expect.
Proven to work
We don’t just take our own word for it. We’ve tested this setup with red teams who try their best to break the system over the phone. In these tests, Raven 3.5 consistently beats other top models when it comes to keeping data safe and following the law.
We also keep your data clean from the start. Our setup uses a strict checking layer for every instruction, and our redaction tool automatically swaps out private customer details for fake data. This means the model learns how to talk like a human without ever seeing your customers' private lives.
Ready to move beyond the rulebook? Speak to our team today
