AIW-303Audit & Assurance Operations
AI in Audit and Assurance: Sampling, Evidence and Workpapers
What you leave with
An AI-assisted audit procedure: attributes per control, populations reconciled to independent sources, documented sampling, what AI does and must not do, how the auditor checks each output, how findings and cause are established, documentation of AI use, and test cases.
Same obligation, two realities
Early-stage and SMESmall team: one or two auditors, so AI reads and drafts and the auditor checks every result before it is recorded.
Enterpriselayered supervision, so evidence-linked results, reviewer re-performance, quality metrics and test cases keep every layer honest.
How the course runs
- The obligation: where the responsibility comes from, cited by section.
- Two realities: how it is met in a small organisation and in an enterprise.
- The method: step-by-step practice with templates and edge cases.
- Paired labs: complete the one matching your work, read the other.
- Artefact and assessment: submit the artefact; a rubric and a short scenario quiz decide the certificate.