Traditional guardrails are trained on attacks that stopped working months ago. Cygnal is trained on the ones working right now, sourced from over 15,000 adversarial red-teamers from around the globe.
Cygnal monitors every prompt, response, and tool-call at runtime, blocking threats in real time with the lowest bypass rate in the industry.
Cygnal sits inline: between users and your model, between your agent and the tools it calls, between your retrieval pipeline and the content it returns. It classifies adversarial inputs and unsafe outputs in real time, with latency low enough for production traffic and recall high enough to catch what actually matters.
Direct and indirect, including injections delivered through retrieved content, tool output, and document context.
Attempts to bypass safety training or system guardrails, including current generation techniques that defend naive classifier defenses.
Model behavior that violates your policies, regardless of how the input was framed.
Unauthorized tool use, scope violations, and adversarially-induced action sequences.
Most traditional guardrails are trained on public attack datasets. The ones that were already outdated when the model shipped.
Cygnal is continuously updated by the team that breaks current-generation models for a living. As the attack landscape moves with every model release, Cygnal moves with it.
You're running customer-facing chat, internal copilots, agentic workflows, or document processing, and you need runtime protection that keeps pace with real threats, not checkbox compliance.
Your AI touches sensitive data, regulated workflows, or high-impact decisions. A missed classification isn't an inconvenience, it's a breach, a violation, or a headline.
Your failure modes aren't limited to "model says something bad." They include "model calls an API it shouldn't" and "model executes a workflow it was manipulated into." Cygnal governs the full action chain.
Cygnal is continuously trained on the attacks actually breaking models right now. See it live.
