Use case DiscoveR AgentIQ

Know which attacks your AI would actually survive

An adaptive RL engine mapped to MITRE ATLAS and OWASP LLM Top 10, re-run on every change, with a live coverage matrix.

Leaves proof it can never erase

CISOSOC / IRCyber vendors & MSSPsSystem integrators

The problem

A point-in-time pentest is stale the week after it's filed. New jailbreaks land daily.

The pentest you filed last quarter is already stale; the jailbreak that lands tomorrow was never on it.

Mirror restores Proof · Evidence · leaves proof it can never erase

How it works

01

Probe

DiscoveR's adaptive RL engine attacks your AI continuously, generating new variants instead of running a fixed checklist.
02

Map

Findings land on a live coverage matrix keyed to MITRE ATLAS and the OWASP LLM Top 10.
03

Re-run

Every change re-triggers the engine. Coverage is current, not a point-in-time snapshot.

"Trust us, we logged it" doesn't pass a regulator anymore. You need signed, tamper-evident proof for every decision.

Pre-prod findings die in a Jira backlog. Runtime sees attacks the red team never imagined.

When an agent does something it shouldn't, your IR team is left guessing across logs that were never written for them.

Get started

FHE-native inference. Runtime agent guardrails. Continuous red teaming. One platform. Book a working session with the team.