How can AI support control testing?
AI can support control testing by reading evidence, matching it to the control criteria, identifying exceptions, preparing the rationale and linking the conclusion back to the source material.
It can work across documents, spreadsheets, screenshots, system exports and client responses. For a recurring control, it may help organise the population, match selected items to evidence and flag missing or inconsistent support. For design and implementation work, it can identify the relevant passages and prepare a structured first-pass assessment.
The auditor still needs to determine the procedure, population, sample and evidence requirements. AI should not quietly change the test or infer that a missing document means the control failed. It should make the gap visible and allow the auditor to request further evidence or reach the appropriate conclusion.
The practical benefit is a cleaner evidence chain. Reviewers can see what was tested, what evidence was used, what exceptions arose and why the preparer reached the conclusion, without reconstructing the work from several disconnected files.
Platformed capability: Platformed brings the test criteria, selected evidence, exceptions and rationale into one reviewable control record. It prepares the assessment and findings; the auditor determines the procedure, sample and final conclusion.
References: IAASB automated tools and techniques · Platformed control assurance