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PCAOB Standards and AI in Audits
PCAOB standards neither ban AI nor give it special approval.
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Descripción general
They apply the same rules on evidence, supervision and documentation to technology-assisted procedures as to manual ones. An audit firm that uses AI is still responsible for three things: the data the tool reads must be reliable, the procedure must fit its purpose, and the work papers must let an experienced auditor understand and re-create what was done. This matters because investors rely on audit opinions, and a fast automated procedure does not make a weak conclusion any stronger.
Buceo profundo
The Public Company Accounting Oversight Board (PCAOB) sets auditing standards for audits of companies registered with the SEC. It regulates the work, not the tool. Several standards govern any procedure, whether a person does it or software does. AS 1105 (Audit Evidence) requires evidence to be sufficient and appropriate, and appropriateness depends on relevance and reliability. AS 2301 sets out how the auditor responds to assessed risks. AS 1201 covers supervision. AS 1215 (Audit Documentation) requires work papers that would let an experienced auditor with no prior link to the engagement understand the work performed, the evidence obtained and the conclusions reached. In 2024 the PCAOB adopted amendments to AS 1105 and AS 2301 that deal directly with technology-assisted analysis of information in electronic form. They take effect for audits of fiscal years beginning on or after December 15, 2025. The amendments make three points. When an analysis serves more than one purpose, such as risk assessment and substantive testing, the auditor must meet the objective of each purpose. The auditor must evaluate the reliability of electronic information, including information the company received from outside sources. And items that a tool flags as meeting the auditor's criteria must be investigated. They cannot be set aside. The PCAOB has also said publicly that its staff is monitoring how firms use generative AI. Its broader quality control standard, QC 1000, treats technological resources as part of a firm's quality control system. Two misconceptions are common. The first is that testing 100% of a population removes the need for judgment. A full-population test built on incomplete or altered data is still unreliable, and the flagged items still need to be evaluated. The second is that AI output counts as audit evidence in its own right. The evidence is the underlying information plus a procedure that was designed, performed and reviewed properly. A summary or score produced by a model is only as good as the checks performed on it.
Impacto Estratégico
Riesgo y seguridad
Los daños catastróficos y cotidianos de la IA dependen de quién comprende los riesgos y quién puede actuar.
Decisiones más claras
La alfabetización pública y profesional determina si es políticamente posible una política de seguridad sólida.
Cortando el bombo
Las explicaciones claras reducen la captación por la exageración, las relaciones públicas de laboratorio y el vago teatro de ética.
The Future of PCAOB Standards and AI in Audits
The 2024 amendments give firms a clearer framework for data analytics, and the first audits under them will show how inspectors read the investigation requirement for flagged items. Generative AI raises questions the current standards answer only indirectly. Examples include how to document a model's role in drafting memos, and how much re-checking of extracted content is enough. The PCAOB has signalled interest through staff outreach, but a firm should not assume new guidance is coming on any particular timeline. For now, the safest approach is to treat every AI-assisted step as a procedure that must be relevant, reliable, supervised and documented under the existing standards.
Implementación en el mundo real
A team runs an analysis over every revenue journal entry for the year and flags entries posted on weekends by users who rarely post to revenue. The flagged entries must then be followed up, because running the analysis does not by itself count as evidence.
An engagement team uses a generative AI tool to pull renewal, termination and pricing terms out of 300 customer contracts. Before relying on the extracted terms, it checks a sample of them against the signed contracts.
Before feeding the company's system-generated aged receivables report into an analytics tool, the auditor tests whether the report is complete and accurate, because it counts as information produced by the company.
A reviewer's work papers record the tool version, the parameters used, the data source, and a reconciliation of the extracted ledger to the trial balance, so another auditor could re-perform the analysis.
Riesgos y barandillas
Tratar el riesgo existencial como ciencia ficción mientras que la capacidad se agrava.
Confundir la seguridad del producto superficial con la alineación en condiciones de alta autonomía.
Dejando a las audiencias que no hablan inglés ni a expertos solo con fuentes de baja calidad.
Hoja de ruta de implementación
Separe los riesgos de daños al producto, mal uso y pérdida de control/desalineación.
Pregunte qué evidencia cambiaría su opinión sobre los plazos y la gravedad.
Prefiera fuentes primarias y evaluaciones concretas a afirmaciones de marketing.
Identifique un camino de acción: carrera, política, financiamiento o habilidades, no solo concientización.
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Preguntas frecuentes
What is PCAOB Standards and AI in Audits?
PCAOB standards neither ban AI nor give it special approval. They apply the same rules on evidence, supervision and documentation to technology-assisted procedures as to manual ones. An audit firm that uses AI is still responsible for three things: the data the tool reads must be reliable, the procedure must fit its purpose, and the work papers must let an experienced auditor understand and re-create what was done. This matters because investors rely on audit opinions, and a fast automated procedure does not make a weak conclusion any stronger.
Which two PCAOB standards did the 2024 amendments on technology-assisted analysis change?
The 2024 amendments changed AS 1105 (Audit Evidence) and AS 2301 (responses to assessed risks). They address how auditors evaluate electronic information and how they use technology-assisted analysis.
An analysis of all revenue journal entries flags 40 entries that meet the auditor's criteria. What does the amended guidance expect?
The amendments make clear that items identified as meeting the auditor's criteria must be investigated. Producing a list of flags is not a completed procedure.
Before putting a company-generated aged receivables report into an analytics tool, what should the auditor evaluate?
A company-produced report is information produced by the company. Its reliability, including its completeness and accuracy, must be evaluated before the auditor relies on it.
Under AS 1215, who should be able to understand the work papers for an AI-assisted procedure?
AS 1215 sets the experienced-auditor test. Documentation must let such a person understand the work performed, the evidence obtained and the conclusions reached.
A single technology-assisted analysis is used for both risk assessment and substantive testing. What must the auditor do?
The amendments say that when an analysis serves multiple purposes, the auditor must achieve the objective of each one. Substantive testing usually demands more precision than risk assessment.
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