Platformed Audit AI Answers

AI audit software uses artificial intelligence to perform defined parts of audit work: reading evidence, preparing documentation, identifying relevant risks and controls, testing information against a methodology, and drafting outputs for auditor review.

AI is most useful in external audit where there is a large amount of evidence to read, structure and test, but the final conclusion still needs professional judgement.

An AI auditor is software that can complete defined audit tasks using client evidence, an audit methodology and a structured review workflow.

Agentic AI can carry out a sequence of connected tasks towards a defined objective, rather than responding to one prompt at a time.

AI can already automate substantial parts of evidence collection, document review, process documentation, risk identification, control assessment, testing preparation and report generation.

No. AI will replace a meaningful amount of manual audit work, but it will not replace the accountability, judgement and professional scepticism of the auditor.

General-purpose AI is designed to help with a very wide range of tasks. Audit AI is configured around specific audit workflows, evidence requirements, methodologies and review controls.

Start with one real audit workflow where the problem is clear, the evidence is available and the result can be reviewed.

Audit risk assessment is the work an auditor performs to understand the entity, identify where a material misstatement could arise and assess the risks that should shape the rest of the audit.

ISA 315 (Revised 2019) places greater emphasis on understanding how an entity uses information technology and how that use affects the financial reporting system, relevant controls and risks of material misstatement.

SAS 145 strengthened the auditor's work over relevant controls, including controls that address risks arising from the entity's use of IT.

SAS 145 is scalable. A less complex entity does not need the same volume or depth of risk-assessment work as a complex group, but the auditor still needs an evidenced understanding of the entity, its information system, relevant controls and risks of material misstatement.

SAS No. 145 is the AICPA's risk-assessment standard for US audits of nonissuers.

Auditors should scope the systems that initiate, process, record or report information relevant to significant classes of transactions, account balances and disclosures.

An automated control is a control performed by a system without the same recurring human intervention as a manual control.

Audit context should be captured once and used wherever it is relevant. Process understanding, system scoping, risks, controls and evidence are connected; treating them as separate documents creates duplication and inconsistency.

Process and controls understanding is the work an auditor performs to understand how a significant business process operates, where a material misstatement could arise and which controls are intended to prevent or detect it.

An audit walkthrough follows a transaction or process from beginning to end so the auditor can understand how the process actually operates, which systems and people are involved, and whether relevant controls have been implemented.

AI can automate much of the work that happens after a walkthrough: transcribing the discussion, separating process steps from commentary, identifying systems and roles, drafting the narrative, producing a process map and linking risks and controls to the underlying evidence.

Yes. AI can turn a walkthrough recording into a structured process narrative, provided the recording is complete enough, the client has consented to its use and the auditor reviews the output.

Auditors can create process maps automatically by using AI to extract the sequence of activities, decisions, systems, roles and control points from walkthrough evidence and convert them into a visual flow.

Risks and controls are identified by understanding each stage of the process, considering what could go wrong in relation to financial reporting, and determining which activities prevent or detect that outcome.

A design and implementation assessment considers whether a control is capable of addressing the relevant risk and whether the control has actually been put into use.

Better process understanding improves audit quality because it gives the team a stronger basis for identifying risks, evaluating controls and designing procedures that respond to how the client actually operates.

IT general controls, usually shortened to ITGCs, are controls over the technology environment that support the reliable operation of systems and automated controls.

ITGCs matter because financial information, automated controls and system-generated reports depend on technology continuing to operate as intended.

The ITGC domains most often considered in financial statement audit are access management, program changes, computer operations and aspects of system development or implementation.

Auditors should scope ITGC work from the financial reporting risks and technology dependencies, not from a generic list of systems or controls.

IT complexity is assessed by considering how many systems support financial reporting, how those systems interact, how much they are customised and how dependent the business and audit are on automated processing.

An IT audit specialist should be involved when the nature or complexity of the technology, the planned reliance or the engagement risk requires knowledge beyond the competence of the core audit team.

Yes. Financial audit teams can perform proportionate ITGC assessments for straightforward environments if the firm gives them a clear methodology, suitable training, structured evidence requirements and access to specialist support when needed.

IT application controls, or ITACs, perform specific processing functions within an application.

Control assurance is the work performed to understand whether controls are suitably designed, implemented and, where relevant, operating effectively enough to support a conclusion.

Control design asks whether the control is capable of addressing the relevant risk.

An auditor should plan to rely on controls when the controls are relevant to the assessed risk, are suitably designed and implemented, and testing their operating effectiveness is expected to produce an effective audit response.

Yes. When the auditor obtains sufficient appropriate evidence that relevant controls operated effectively, the planned substantive response may be reduced or made more targeted.

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.

AI should make missing or contradictory evidence visible, not resolve the problem by guessing.

Firms can standardise control testing by defining a common methodology, evidence requirements, decision points, review workflow and output structure, then making those standards easier for every team to apply.

Yes. AI-assisted work can go on the audit file if it documents the work performed, evidence used, judgements made and conclusions reached to the standard required by the firm's methodology and applicable auditing standards.

An AI-generated audit output is defensible when a reviewer can understand what the system did, which evidence it used, how the conclusion was reached, what judgement the auditor applied and how exceptions were resolved.

Auditors should review an AI-generated conclusion in the same way they would review other prepared audit work: understand the procedure, inspect the relevant evidence, challenge the rationale, investigate exceptions and decide whether the conclusion is appropriate.

Audit firms should manage hallucination risk by constraining the AI to relevant evidence, requiring source citations, making uncertainty visible and keeping an auditor responsible for the conclusion.

Human oversight means auditors define the work, review the evidence and conclusions, resolve exceptions and approve the final output.

Professional scepticism should be applied to AI output in the same way it is applied to other audit evidence: with a questioning mind, attention to possible bias and a willingness to investigate information that contradicts the expected conclusion.

An audit firm should document enough for an experienced reviewer to understand how AI affected the procedure, what evidence was used, how the output was evaluated and who remained responsible for the conclusion.

An audit firm should validate an AI tool against its intended use, methodology and risk before allowing it into live engagement work.

A focused AI audit workflow can begin producing value quickly, but a responsible firm-wide rollout takes longer than switching on software.

Yes. Purpose-built AI audit software should be configurable to the firm's methodology rather than forcing every firm into a generic control library, risk model or review process.

Yes. AI audit software can sit alongside systems such as Caseware, CCH and other audit-file platforms, provided the workflow supports suitable imports, exports or direct integrations.

AI audit software needs the evidence relevant to the procedure it is performing.

Client audit data should be protected through the same disciplined controls expected for other confidential engagement systems, with additional attention to how AI providers process and retain information.

Data residency describes the country or region in which data is stored. For audit firms, it can affect client confidentiality commitments, sector requirements, procurement decisions and the expectations of local regulators or network firms.

A firm should roll out audit AI through a controlled operating model: common methodology, representative pilots, clear ownership, role-based training, visible support and ongoing quality monitoring.

AI audit software can remove a large share of the time spent collecting, reading, structuring and documenting evidence.

An audit firm should calculate AI ROI by comparing the full cost of the current workflow with the cost and value of the AI-assisted workflow across a representative engagement portfolio.

AI can improve both quality and efficiency, but quality gains only appear when the workflow is designed around evidence, methodology and review.

AI can create more client value by reducing administrative friction and turning audit evidence into clearer insight about processes, risks and controls.

An accounting firm should evaluate AI audit software against the quality of the audit work it produces, not the fluency of the demonstration.

Platformed is most differentiated by its focus on automating evidence-heavy audit work across ITGCs, process understanding, risk and control assessment, with conclusions linked to source evidence for auditor review.