AIAC-01 · Foundation track
Certified AI Assurance Practitioner
The baseline credential for professionals entering AI assurance: risk identification, testing concepts, governance frameworks, and evidence-based assessment across any industry.
- Reference
- AIAC-01
- Level
- Foundation
- Examination
- 3 hours
- Delivery
- Remote, proctored
- Credential
- Valid 3 years
§ 1 — Intended candidates
Who this certification is for
Risk, audit, compliance, data science, and engineering professionals taking on AI assurance responsibilities for the first time.
- Prerequisites
- No formal prerequisites. Candidates are expected to have professional experience in a risk, technology, audit, or compliance function.
- Status
- In development · First cohort October 2026
§ 2 — Examination domains
What the examination covers
Domain weightings are published with the full syllabus ahead of the first cohort.
- 01
AI risk identification
Establishing system boundaries, identifying affected parties, and distinguishing model risk from deployment risk in a specific operating context.
- 02
Governance frameworks
Working knowledge of ISO/IEC 42001, the NIST AI RMF, and ISO/IEC 23894, and the judgement to apply the right instrument to the right question.
- 03
Testing and evaluation
Evaluation design, benchmark limitations, red-teaming concepts, and what constitutes sufficient evidence for a given risk tier.
- 04
Documentation and evidence
Producing model documentation, risk profiles, and assessment records that withstand independent review.
- 05
Assurance in practice
Scoping an engagement, reporting findings to non-technical stakeholders, and tracking remediation to closure.
§ 3 — Assessment
How candidates are assessed
Scenario-based examination. Candidates produce a structured AI risk profile for a supplied deployment context, prioritize applicable requirements, and defend an implementation plan.
Successful candidates receive a credential with a unique identifier that any employer can check against the public record. See credential verification.
Curriculum mapping indicates alignment of learning content with published frameworks. It does not constitute endorsement by, or certification under, any standards organization or regulator.
§ 4 — Further reading
Background reading
- What is AI assurance? A practitioner's definition
AI assurance is the practice of producing credible, independently reviewable evidence that an AI system works as intended. A working definition, and how it differs from governance and compliance.
- AI assurance vs AI audit vs AI risk assessment: the differences that matter
Three terms used interchangeably that mean different things, have different outputs, and require different independence. A practitioner-level distinction.
- ISO/IEC 42001 vs NIST AI RMF: which framework, when
One is a certifiable management system standard; the other is a voluntary risk framework. They are not alternatives, and choosing between them as if they were is the common mistake.