Audit Quality Control, QC 1000, and Gen AI in Client Data
Honors Capstone Project
1
Advisor(s)
Dr. Jill Cadotte
Confirmation
1
Document Type
Paper
Location
ONU McIntosh Center; Dean's Heritage
Start Date
21-4-2026 2:45 PM
End Date
21-4-2026 3:00 PM
Abstract
This project researches the quality control (QC) responses public accounting firms can implement in response to risks of generative AI use in client accounting data. The structure of the project follows the new PCAOB standard QC 1000, which outlines quality objectives, risks to quality objectives, and responses to quality risks. Perspectives on responses will be gathered from audit professionals familiar with their firm’s QC system and subsequently analyzed and compiled for a series of best practices.
Recommended Citation
O'Neill, Zachary, "Audit Quality Control, QC 1000, and Gen AI in Client Data" (2026). ONU Student Research Colloquium. 38.
https://digitalcommons.onu.edu/student_research_colloquium/2026/Papers/38
Restricted
Available to ONU community via local IP address and ONU login.
Audit Quality Control, QC 1000, and Gen AI in Client Data
ONU McIntosh Center; Dean's Heritage
This project researches the quality control (QC) responses public accounting firms can implement in response to risks of generative AI use in client accounting data. The structure of the project follows the new PCAOB standard QC 1000, which outlines quality objectives, risks to quality objectives, and responses to quality risks. Perspectives on responses will be gathered from audit professionals familiar with their firm’s QC system and subsequently analyzed and compiled for a series of best practices.