Knowledge Graphs Can Improve GenAI Applications in Audits

SUMMARY Auditors conducting risk assessment can use Generative AI (GenAI) to analyze large volumes of complex information from multiple sources. However, the technology underlying GenAI tools often struggles to retain and learn from feedback. This commentary proposes that auditors can address these limitations by organizing audit-relevant documents in knowledge graphs whose structured context persists across GenAI interactions. Auditors using knowledge graphs gain traceable reasoning paths that support explainability, constrain GenAI retrieval to relevant evidence, and reduce computational costs by limiting the documents processed. This commentary also discusses how knowledge graphs can be integrated into existing audit workflows under current auditing standards. JEL Classifications: M42.