Event Type: Virtual
Cost: Member Pricing – FREE / Non-Member Pricing – $30
CPEs Available: 1
AI is everywhere in audit conversations but for many teams, it still raises more questions than confidence. Concerns about data security, verification effort, and increased review workload have slowed adoption, especially in environments where quality and traceability are non-negotiable.
This session cuts through the noise to separate common myths about AI in audit from how leading teams are actually using it today. We’ll examine why some AI tools increase audit friction, what “audit-grade” AI really means, and how embedding AI into existing workflows can reduce rework without compromising professional judgment or control.
Designed for audit leaders and practitioners alike, this webinar offers a practical, risk-aware perspective on AI adoption focused not on replacing auditors, but on removing the manual work that quietly creates review bottlenecks and burnout.
Learning Objectives
By attending this session, participants will be able to:
- Distinguish between AI approaches that reduce audit risk versus those that introduce rework and review friction
- Understand how AI can support consistency, traceability, and reviewer confidence without replacing professional judgment
- Identify the most common misconceptions around AI verification, security, and “black box” outputs
- Evaluate what criteria matter most when assessing AI tools for audit-grade use
Presenter
Rachel Leininger, Solutions Engineer at DataSnipper
Rachel Leininger is the Senior Solution Engineer at DataSnipper, where she leads initiatives to enhance audit efficiency and compliance through intelligent automation. Prior to joining DataSnipper in February 2023, she served at KPMG US from 2016 to 2023, holding roles such as Senior Audit Associate, Audit Manager and Audit Continuous Improvement Manager. Rachel is a licensed Certified Public Accountant in New York and earned her Master’s in Accounting from The University of Texas at Arlington.
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