Independent AI Security, AI Governance and Enterprise AI assurance — adversarial red-teaming and EU AI Act readiness for systems running in Spanish and English. Don't take our word for it: run real attacks against a live agent.
TELUS Digital's Fuel iX research benchmarked 24 frontier models configured as production customer-service bots — 750 adversarial scenarios, ~399,000 evaluations. The verdict: every single model was exploitable, with attack success rates from 1% to 64% under identical instructions.
“Single-try validation creates dangerous false confidence.”
— TELUS Digital · Fuel iX, State of GenAI safety and security
Adversarial red-teaming of your GenAI apps — prompt injection, jailbreaks, system-prompt extraction, PII leakage and out-of-policy generation — run as real attacks, not checklists, with reproducible PASS/FAIL evidence and severity.
EU AI Act readiness mapping and NIST AI RMF alignment, plus the documentation a regulated deployment needs — risk classification, transparency, logging, human oversight. Readiness, not certificates we don't issue.
Assurance wired into how you ship — continuous, automated testing on every model or prompt change, gating CI and tracking drift, so a system that passes today stays safe after the next deploy.
Probabilistic systems need adversarial, repeated, language-aware testing — a discipline borrowed from safety-critical software.
Per system: which attack classes apply, in which languages, against which data.
Native ES/EN adversarial tests against the live agent. Rule-based verdicts first; LLM-judge second.
Severity-weighted score plus an EU AI Act readiness view. Every FAIL is reproducible.
Wire it into CI so regressions block the deploy, and re-run on drift.
// red · blue · purple team approaches — reproducible tests, explicit verdicts, no claim without evidence.
We'll run the battery against your real agent — in your languages — and walk you through every finding.