Protection · Evidence · Trust
Every AI system you deploy makes decisions in your name — and you carry the consequences. We show you how your AI can fail, what those failures can affect, and give you verifiable evidence to act before they become consequences.
Your AI answers your customers, moves your money, reaches your data, operates your tools and speaks with your authority — at a scale no human review can keep up with.
So there are two things to protect. The system itself, against prompt injection, jailbreaks, poisoned context and tools, and actions it was never authorized to take. And everything it can reach: the people, the data, the decisions, the assets, the processes, the reputation that stands behind it.
The gap isn’t intent — you already mean to use AI well. The gap is evidence: something concrete you can point to that shows how your AI actually behaves, and what that behavior can reach.
8
Dimensions of behavior, each scored and independently traceable
5
Framework axes the same evidence projects into — threat, security, risk, governance, regulatory
N runs
Every risk is an occurrence rate you can inspect, never a single anecdote
The Methodology
Our 8-dimension framework was developed by PhD researchers and validated through peer-reviewed publications.
Because the AI systems we test are non-deterministic, we report registered, hash-citable attack traces and statistical evidence: the rate at which each failure occurs across N runs, with Wilson confidence intervals and Rogan-Gladen correction for judge reliability.
When a budget limits coverage, we report exactly what was measured and what wasn’t. Every score traces back to the attack that produced it — which is also what lets an auditor verify it later, if you need it to.
Explore the framework →Research published in AI bias, compliance frameworks, and ethical evaluation methodology
In-house researchers with doctoral expertise in AI/ML and ethics
Ongoing partnership with universities and research centers for methodology validation
Every scoring criterion is documented, versioned, and publicly auditable
The Framework
Each dimension scores your AI system’s behavior and stays independently traceable to the run that produced it. The dimensions classify the evidence — they are not a framework. That same evidence then projects into the threat, security, risk, governance and regulatory frameworks you report against, none of which decides what we are able to see.
01
Detects bias, stereotyping, and unequal treatment across protected groups, using statistical and counterfactual testing
→ Disparate impact
02
Surfaces hate, violence, self-harm, sexual content, and dangerous instructions — including harm that carries no obviously toxic wording
→ Harmful output
03
Measures unfaithful explanations, sycophancy, and undisclosed AI — whether a decision can be understood by the people it affects
→ Unfaithful explanation
04
Probes PII leakage, training-data extraction, and system-prompt disclosure across your data-governance boundaries
→ Data exposure
05
Verifies outputs against source material to catch hallucination, fabricated citations, and unsupported claims
→ Fabrication
06
Tests resistance to prompt injection, jailbreaks, encoding tricks, and adversarial suffixes that bend the model's behavior
→ Adversarial input
07
Exercises unauthorized actions, privilege abuse, identity spoofing, and tool misuse wherever your AI can act, not just answer
→ Unauthorized action
08
Checks oversight saturation, governance evasion, and whether every action stays traceable to the human accountable for it
→ Oversight failure
Every finding is traceable to the attack that produced it. Every score is a rate you can inspect, not an opinion you have to trust.
Grounded Adversarial Testing
We generate adversarial probes grounded in your system’s own knowledge base — the articles of the law you operate under, the products in your catalog, the policies you publish. Each probe exercises a failure that actually matters in your domain, and every attack becomes a registered, hash-citable trace.
The Evidence Architecture
Everything we observe converges into one preserved object: what happened, how often across N runs, and the trace that proves it. From there the same evidence reads five different ways — as a threat, as a control failure, as risk, as a governance requirement, as a legal obligation. Each reading is a projection. None of them is the structure underneath.
What we observe
Grounded probes from your own world
Multi-turn adversarial attacks
Tool invocations and agent actions
Model outputs and refusals
Administrative attestations
Classified as
Illustrative example
The agent invoked the payment tool without valid authorization.
Occurred in 7 of 40 runs · coverage 82%, 9 probes not run · trace 4f2a…c19
Change a framework and only the reading below changes. The evidence above it does not move.
Threat
MITRE ATLAS
at a pinned snapshot
the adversarial technique used
Security & control
OWASP Agentic
at a pinned edition
agent authorization and control failure
Risk
NIST AI RMF
version 1.0
unauthorized financial action and its impact
Governance
ISO/IEC 42001
2023 edition
the control requirements this speaks to
Regulatory
EU AI Act
at the applicable text
the obligation that may apply in context
What you change, what you govern, and when your AI deserves your trust.
What We Build
Proof protects the AI that acts in your name. Check protects what you put your name behind. Different objects, one discipline: observe, verify, preserve the evidence, understand the exposure, support the human decision. Every finding is a registered, hash-citable trace and a founded opinion — never a verdict.
Proof
Red-team your AI
Adversarial red-teaming of your AI system. We generate multi-turn attacks, then type and score what breaks — by dimension, with a coverage report and an occurrence rate over N runs.
What you get
Ideal for
Teams putting AI in front of customers or regulators.
Check
Verify your content
Regulatory verification of your content and documents, human- or AI-authored. We hold each one against the requirements of the regulation it must answer to, and ground every objection in a citable source.
What you get
Ideal for
Anyone who submits content to a regulator, court, or board.
A quantified risk posture across every AI system, defensible with the trace behind each score.
EU AI Act and GDPR requirements matched to technical and administrative evidence in a single view.
API-first, grounded adversarial testing on your own systems, with no latency impact in production.
The evidence to decide when your AI deserves your trust — and to say why, on the record.
Start with a Proof red-team of the AI you run, or a Check review of the content you stand behind. We won’t tell you to trust your AI — you leave with the evidence to decide when you should.