Regulators, customers, boards, and courts all ask the same question of an AI system: who checked, and can they prove it? AxiLayer AI performs independent, evidence-based audits of AI systems — algorithms, data, controls, and documentation — producing findings that stand up because the examiner had nothing riding on the answer.
"AI audit" is used loosely in the market — sometimes meaning a questionnaire, sometimes a demo. Used properly, it means what audit has always meant: independent examination of evidence against criteria, with findings the auditee doesn't get to edit. Our audits work through four layers:
Performance validated against the claims made for it; fairness and bias measured across the populations the system actually affects; robustness probed under distribution shift and adversarial conditions; explainability tested against the oversight duties the deployment carries.
Provenance and rights for training, validation, and operational data; representativeness for the deployment context; quality and error handling; the governance record the EU AI Act's Article 10 and comparable regimes demand.
Human oversight as practiced, not as drawn; event logging exercised end to end; access and change management; incident detection and response. Controls are tested by operation — we run them, we don't read about them.
Technical files, risk assessments, model cards, instructions for use — checked for existence, currency, and truth. Documentation that contradicts observed behavior is itself a finding, and a common one.
Every tested control is mapped across the frameworks in scope, so one audit produces evidence reusable under the EU AI Act, NIST AI RMF, ISO/IEC 42001, and the rest of the register.
Internal reviews are necessary and insufficient. The audiences an AI system must satisfy — market-surveillance authorities, procurement teams, audit committees, insurers, opposing counsel — apply a simple discount: evidence produced by the party being examined is testimony, not proof. Independence converts testimony into proof.
Independence at AxiLayer AI is structural, not rhetorical. We do not build the systems we audit, we do not take success fees on findings, our methods are disclosed in every report, and our practice is aligned to ISO/IEC 17020 — the international standard governing the impartiality and competence of inspection bodies. Where a formal conformity assessment or certification is required, we prepare you for the designated body's examination; our value is that nothing in that examination surprises you.
The same structure serves government. AxiLayer AI is SAM.gov registered and delivers audit work in cloud, on-premises, and air-gapped environments — the details are in our government capability profile.
Systems, frameworks, and depth agreed in writing: which models, which jurisdictions' rules, which controls. Ambiguous scope is how audits become theater; ours begins with a criteria register both sides sign.
Certified human auditors examine the four layers — algorithm, data, controls, documentation — with testing performed in your environment or ours, including air-gapped delivery where the data cannot travel.
An audit report with severity-rated findings, the evidence behind each, and a remediation plan sequenced by risk. Management responses are recorded alongside findings, the way audit committees expect.
Where the engagement supports it, an AxiLayer Compliance Passport documents what was examined and when — verifiable online by any third party. AxiSentinel then keeps the audited state monitored between engagements, because models change faster than audit calendars.
A scoping briefing defines which systems and frameworks belong in your first audit, and what evidence you'll hold at the end.
