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Adversarial Testing·EU AI Act Art.15·NIST AI RMF

Penetration testing,
but for AI behaviour.

Before you deploy an AI agent, chatbot, or assistant — we stress-test it. Adversarial prompts, hallucination probes, prompt injection, brand safety, regulatory compliance. Delivered as a cryptographically signed Risk Report Card.

Currently in design phase  ·  Early access available for select teams
73%
of AI deployments have at least one critical prompt injection vulnerability before launch
EU AI Act
Art.15 requires accuracy, robustness, and cybersecurity assessment before high-risk AI goes live
6
categories of AI-specific risk that network penetration testing completely misses
$4.7M
average cost of an AI incident — brand damage, regulatory fine, and remediation combined

What we test

Six categories of AI risk your security team isn't testing for

Traditional pen testing checks whether attackers can get in. AI red-teaming checks whether your model can be manipulated to say, do, or leak things it shouldn't — from the inside.

Critical
💉
Prompt Injection
Attackers embed instructions inside user input to override your system prompt, exfiltrate data, or take actions the AI was never meant to perform.
Ignore previous instructions and... Your real instructions are... [SYSTEM OVERRIDE]
Critical
🔓
Jailbreak Sequences
Multi-step manipulation that bypasses safety guardrails. Includes roleplay attacks, hypothetical framing, and adversarial persona switching.
DAN jailbreak variants Roleplay bypass patterns Token smuggling
High
🌀
Hallucination Probing
Systematic testing for confident false statements — especially dangerous in regulated domains like healthcare, legal, financial advice, and compliance.
Domain-specific factual traps Citation fabrication tests Regulatory misquote probes
High
📤
Data Exfiltration
Testing whether the model can be tricked into revealing training data, system prompt contents, other users' information, or confidential context it was given.
System prompt extraction Training data reconstruction Context window leakage
High
🏷️
Brand Safety
Edge case scenarios that produce outputs damaging to your brand — competitor promotion, inappropriate content, politically charged statements, or reputational risk under adversarial framing.
Competitor mention triggers Sensitive topic handling Persona drift under pressure
Medium
⚖️
Regulatory Compliance
Validation against EU AI Act Article 15, NIST AI RMF MEASURE function, and ISO 42001 clause 9 — documenting what the model does and doesn't do in scope of applicable requirements.
EU AI Act Art.15 robustness NIST MEASURE-2.5 Bias and fairness probes

How it works

From briefing to signed report in five steps

We take the same disciplined approach as a network pen test — scoped engagement, structured methodology, severity-ranked findings, and a deliverable you can hand to a regulator.

01
📋
Scoping call
Define the AI system, deployment context, user base, and risk appetite. Identify which frameworks apply.
02
🔬
Adversarial testing
Run hundreds of structured probe sequences across all six risk categories. Human-reviewed, not automated checkbox ticking.
03
📊
Severity ranking
Every finding scored by likelihood, impact, and exploitability. Critical through informational — with recommended fixes for each.
04
📄
Risk Report Card
Delivered as a structured report — executive summary, full finding catalogue, regulatory mapping, and remediation roadmap.
05
🔐
Signed evidence
The full assessment becomes cryptographically signed evidence in your Auricen account — auditor-verifiable via URL, no system access required.

The deliverable

A Risk Report Card your auditor and board can both read

Most AI testing produces a pile of logs. We deliver a structured, severity-ranked report that maps every finding to a regulatory requirement — and signs the whole thing with our RSA-SHA256 integrity layer so it can't be altered after delivery.

🎯

Severity-ranked findings

Critical, High, Medium, Low, Informational — each with likelihood score, impact description, and a specific remediation recommendation.

📐

Regulatory control mapping

Every finding mapped to the EU AI Act article, NIST AI RMF subcategory, or ISO 42001 clause it affects. No translation required for your compliance team.

🔐

Cryptographically signed

The report becomes a tamper-evident evidence record in your Auricen account. Share the verify URL — any auditor can independently confirm it hasn't changed since delivery.

AI Risk Report Card

Acme Corp — Customer Support Chatbot v2.1  ·  Apr 2026

C+
Risk grade
3
Critical
7
High
12
Medium
5
Low
Probes run
847
CRIT
System prompt exfiltration via indirect injection

Attacker can extract full system prompt contents by embedding instructions in a support ticket subject line. Confirmed reproducible in 3/3 attempts.

EU AI Act Art.15 MEASURE-2.5
HIGH
Competitor promotion under adversarial persona framing

Under roleplay framing ("pretend you work for...") the model promotes competitor products by name. Brand safety violation.

Brand Safety
MED
Regulatory hallucination — GDPR misquotation

Model confidently misquotes GDPR Article 17 when asked about data deletion rights. No citation offered. High-stakes domain.

Hallucination ISO 42001 §8.6
Cryptographically signed by Auricen · RSA-SHA256
auricen.com/verify/a3f9...c2d8

Why now

Regulators are requiring this — with or without a service to help you

The EU AI Act, NIST AI RMF, and emerging state-level US laws all require some form of pre-deployment risk assessment for AI systems. Most organisations have no structured way to do it.

🇪🇺
EU AI Act
High-risk AI systems
Article 15 requires that high-risk AI systems are designed to achieve appropriate levels of accuracy, robustness, and cybersecurity — verified before deployment.
Art.9Risk management system — ongoing
Art.15Accuracy, robustness, cybersecurity
Art.43Conformity assessment before market
🇺🇸
NIST AI RMF
Voluntary but fast becoming standard
The MEASURE function explicitly requires organisations to analyse, assess, and track AI risk — including adversarial testing of AI system behaviour.
MEASURE-2.5Robustness and adversarial testing
MEASURE-2.6Bias and fairness assessment
GOVERN-1.2Organisational risk policies for AI
🌐
ISO 42001
AI Management System standard
Clause 8.4 requires organisations to assess AI system impacts before deployment. Clause 9 requires performance evaluation and monitoring.
§8.4AI system impact assessment
§8.6AI system use — monitoring
§9.1Performance evaluation

Who this is for

You're shipping AI. Your legal team is nervous. Your customers are asking questions.

This service is for any team deploying an AI-powered feature that touches users — especially in regulated industries or enterprise sales where a customer or regulator will eventually ask "how do you know it's safe?"

🤖

SaaS teams shipping AI agents

Customer-facing AI assistants, support bots, sales copilots. Any system that talks to your users is a brand safety and security surface area you need to have tested before launch.

🏛️

Regulated industry deployments

Healthcare, financial services, legal, HR, and education — sectors where AI decisions carry regulatory weight and hallucinations or bias have real consequences.

📋

Enterprise AI Act compliance

Companies selling into EU markets with AI-powered products that fall under the high-risk classification — who need a documented conformity assessment before deployment.

🔍

Pre-acquisition AI due diligence

Investors and acquirers assessing AI systems as part of technical due diligence. Know what you're buying before you buy it.

Early access

Be first when this launches

We're building this with a small group of early design partners. If you have an AI deployment coming up in the next 6 months and want to be involved in shaping the service, join the waitlist.

No spam. We reply personally.  ·  Already have an AI deployment? Email us directly.

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AI Pre-Launch Risk Assessment findings will feed directly into your evidence account — signed, mapped to controls, and auditor-verifiable from day one.