EU AI Act Compliance
Most organizations deploying AI systems cannot produce the evidence regulators now require. We map every AI system to EU AI Act Articles 9–15 using a peer-reviewed, 8-dimension framework — with an immutable audit trail that gives you founded, defensible evidence of readiness, not just claims.
The EU AI Act applies to every organization deploying high-risk AI systems in the European Union. Enforcement is not future tense — it is happening now.
Regulators are not asking whether you have an AI governance policy. They are asking you to show that every AI system has been evaluated, that risks have been documented, and that your evidence is auditable.
“We’re working on it” is not defensible. “Here is our immutable audit trail” is.
Up to 7%
of global annual revenue — maximum EU AI Act fine
$2.3M
average cost of a single AI compliance incident
73%
of enterprises deploying AI have no formal compliance framework
Art. 9–15
the specific requirements your AI systems must demonstrably meet
What Regulators Expect
The EU AI Act defines specific obligations for high-risk AI systems. For each article, we show what the regulation requires and what evidence you need to produce.
Art. 9
Risk Management
What It Requires
A continuous, iterative risk management process throughout the AI system lifecycle. Risks must be identified, evaluated, and mitigated with documented evidence.
What a Regulator Expects
A risk register with quantified risk scores, documented mitigation actions, and proof of ongoing monitoring — not a one-time assessment.
Art. 10
Data Governance
What It Requires
Training, validation, and testing data must meet quality criteria. Data practices must prevent bias and ensure representative datasets across protected groups.
What a Regulator Expects
Demographic parity metrics, bias testing results, and documentation that data governance practices are enforced — not just described.
Art. 11
Technical Documentation
What It Requires
Comprehensive technical documentation that demonstrates compliance before the AI system is placed on the market or put into service. Documentation must be kept up to date.
What a Regulator Expects
Model cards, data sheets, methodology descriptions, and a complete record of compliance evaluations — maintained continuously, not created retroactively when asked.
Art. 12
Record-Keeping
What It Requires
AI systems must have automatic logging capabilities that ensure traceability of the system's functioning throughout its lifecycle. Logs must be retained for an appropriate period.
What a Regulator Expects
An immutable, tamper-proof audit trail that captures every evaluation, every flag, and every decision — with retention periods that exceed the system's operational lifetime.
Art. 13
Transparency
What It Requires
AI systems must be sufficiently transparent to enable users to interpret the system's output and use it appropriately.
What a Regulator Expects
Explainability scores for every AI decision, human-readable justifications for flags and recommendations, and documented evidence that transparency mechanisms are operational.
Art. 14
Human Oversight
What It Requires
AI systems must be designed to allow effective oversight by natural persons during the period in which the system is in use, including the ability to override or reverse automated decisions.
What a Regulator Expects
A dashboard showing human intervention rates, override logs, and evidence that oversight mechanisms are used — not just available.
Art. 15
Accuracy, Robustness & Cybersecurity
What It Requires
AI systems must achieve consistent performance levels, be resilient to errors and adversarial attacks, and meet cybersecurity standards appropriate to the risk level.
What a Regulator Expects
Critical error rates, adversarial resilience test results, performance drift monitoring, and evidence that robustness is continuously measured — not tested once and assumed.
Our 8-dimension framework maps natively to Articles 9–15. Each dimension produces the specific evidence regulators expect — scored, traceable, and stored in an immutable audit trail.
Article
Art. 9 — Risk Management
Dimension
8. Accountability & Human Oversight
Proves
Risk scored per dimension as an occurrence rate over N runs, with documented mitigation and a coverage report
Article
Art. 10 — Data Governance
Dimension
1. Fairness & Non-Discrimination
Proves
Bias, stereotyping, and unequal treatment surfaced across protected groups through statistical and counterfactual testing
Article
Art. 11 — Technical Documentation
Dimension
Platform: Audit Trail
Proves
Documentation generated alongside every run and maintained as part of the immutable audit trail
Article
Art. 12 — Record-Keeping
Dimension
Platform: Immutable Audit Trail
Proves
Registered, hash-citable records with 7+ year retention — every attack, every finding, every score
Article
Art. 13 — Transparency
Dimension
3. Transparency & Explainability
Proves
Unfaithful explanations, sycophancy, and undisclosed AI surfaced, with full traceability from score to the attack that produced it
Article
Art. 14 — Human Oversight
Dimension
7. Security & Access Control
Proves
Unauthorized actions, privilege abuse, and oversight evasion exercised, with every action traceable to the human accountable for it
Article
Art. 15 — Accuracy & Robustness
Dimension
5. Factuality + 6. Robustness
Proves
Hallucination and fabricated citations caught, resistance to prompt injection and jailbreaks tested — expressed as rates over N runs
Additional coverage: Safety & Harmful Content and Privacy & Data Protection provide evidence beyond Articles 9–15, covering GDPR alignment, PII exposure, and harmful content detection.
Peer-Reviewed Methodology
Most AI governance platforms ask you to trust their proprietary compliance engine. When a regulator asks how it works, you point to a vendor’s marketing page.
We do it differently. Our 8-dimension framework was developed by PhD researchers in AI ethics, bias detection, and adversarial evaluation. It is validated through peer-reviewed publications across three domains.
When a regulator asks how you evaluate AI compliance, you point to published science. That is the difference between a vendor opinion and defensible evidence.
Whether you need a readiness baseline or a full on-premise deployment, we deliver the evidence regulators expect.
Proof · Cloud
Your EU AI Act Readiness Baseline
A comprehensive red-team of your AI systems in 3 weeks.
Best for: Organizations that need to understand their EU AI Act readiness before committing to a platform.
Enterprise
Full PlatformProof, On-Premise
The full platform in your own environment, with multi-framework readiness and audit-ready evidence.
Best for: Organizations deploying AI at scale that need multi-framework readiness on premise under the EU AI Act.
“Deployed with a Fortune 500 financial services organization managing 100+ AI systems in a regulated environment. Live in production and producing defensible evidence of readiness.”
8 scientifically validated dimensions. Immutable audit trails. Founded, defensible evidence of readiness mapped to Articles 9–15. Start with a red-team or deploy Proof on premise.