Generative AI has rapidly evolved from an experimental curiosity to a powerful force reshaping the modern enterprise. For internal auditors—those stewards of organizational integrity who often face increasing workloads with limited resources—Generative AI offers a critical opportunity: the chance to automate low-value tasks, augment decision-making, and bring sharper insight into complex environments.
But for many audit professionals, the question remains: How do I use it, exactly?
This article provides a curated set of tested prompts tailored to the internal audit function. Whether you’re a Chief Audit Executive seeking to streamline audit planning or a staff auditor preparing for a walkthrough, these AI-powered prompt structures can save hours of manual effort while enhancing quality and consistency.
Understanding the Use Case
At its core, generative AI like GPT models functions as a highly advanced language processor. Unlike earlier automation tools that required rigid inputs and fixed outputs, today’s AI can flexibly respond to natural language. The result: auditors can engage with it conversationally—asking for summaries, redlines, planning ideas, or risk assessments—without needing programming skills.
However, results vary based on the clarity of your prompt.
Much like a skilled interviewer draws out depth through smart questioning, effective prompt engineering makes the difference between generic fluff and actionable insight. Below, we offer prompt templates aligned to the key stages of the audit lifecycle: planning, fieldwork, reporting, and advisory.
1. Audit Planning: Developing Scoping Questions and Risk Hypotheses
Generative AI can help identify relevant scoping questions and risks based on the business process under review.
Prompt Example:
“Act as an experienced internal auditor. I am planning an audit of the procure-to-pay process in a global manufacturing company. Suggest a list of potential inherent risks, relevant regulations, and 10 scoping questions to help define the audit scope.”
Use Case:
A Fortune 500 audit team used this prompt to quickly draft risk hypotheses for a time-sensitive special audit in Latin America. The AI’s suggestions were cross-checked against prior audits, accelerating the planning process by 40%.
2. Control Testing: Evaluating Control Design and Operating Effectiveness
Internal auditors often evaluate the sufficiency of control design. Generative AI can assist in analyzing whether controls meet objectives and suggest improvements.
Prompt Example:
“Given this control description—‘All invoices above $10,000 require dual approval by Finance and Operations leadership’—evaluate its design adequacy. What potential control gaps or failure points should be considered?”
Use Case:
This helped a Big Four advisory team identify possible segregation of duties concerns across a regional business unit with no consistent approver documentation. The AI also proposed compensating controls and scenarios for walkthrough validation.
3. Interview Prep: Walkthrough and Inquiry Question Generation
Preparing for walkthroughs with process owners can be repetitive and time-consuming. AI can generate tailored interview guides based on control objectives or process narratives.
Prompt Example:
“I am interviewing the payroll administrator to validate timekeeping controls. Generate a list of 8 open-ended questions that assess system access, exception handling, and fraud risk.”
Use Case:
An internal audit team at a healthcare system used this technique to prep junior auditors for walkthroughs with HRIS administrators. It increased the depth of inquiry and gave new team members more confidence in client interactions.
4. Fieldwork Analysis: Summarizing Evidence and Identifying Themes
Internal auditors are awash in evidence—meeting notes, system reports, control test results. AI can assist in synthesizing large volumes of unstructured data.
Prompt Example:
“Summarize the following testing evidence across 15 control activities. Highlight recurring deficiencies, trends by department, and any compliance implications. Output in bullet points suitable for a working paper.”
Use Case:
In a complex SOX testing engagement, this prompt was used to digest over 30 pages of control test results and helped the senior auditor draft initial findings in under an hour.
5. Reporting: Drafting Audit Findings and Recommendations
Perhaps one of the most immediate value-adds: AI excels at turning notes into formal prose.
Prompt Example:
“Draft a management-level audit finding for the following issue: lack of reconciliation between warehouse inventory records and the ERP system. Include condition, cause, impact, and recommendation. Use professional and neutral language appropriate for board reporting.”
Use Case:
This saved one publicly traded tech company’s audit team from rewriting redundant language across 15 findings. AI provided a consistent tone and structure that aligned with the company’s reporting standards.
6. Advisory Engagements: Drafting Controls for Emerging Risks
When supporting new initiatives—such as ESG reporting or AI governance—internal auditors may be asked to design first-line controls from scratch.
Prompt Example:
“Suggest five preventive and five detective controls to manage the risk of unauthorized use of generative AI tools by employees in a financial services firm. Include a brief explanation of each.”
Use Case:
One global bank’s audit innovation team used this prompt to workshop a governance framework, which was later adopted by the information security group in their official AI Acceptable Use Policy.
Risks and Ethical Considerations
While the benefits are clear, internal auditors must remain vigilant. AI tools are only as good as the data they’re trained on—and they don’t understand context like a human does. Here are a few principles to keep in mind:
- Confidentiality: Never input sensitive client data into public AI tools. Use enterprise-grade solutions with appropriate safeguards.
- Professional Judgment: Treat AI as a supplement, not a substitute. All AI-generated outputs should be reviewed by qualified professionals.
- Audit Trail: Document how AI was used in audit execution to maintain transparency and traceability.
Final Thoughts
Generative AI is not a panacea—but it is a multiplier. For internal audit functions that embrace it responsibly, it can unlock new levels of efficiency, consistency, and strategic insight.
The future of audit isn’t about replacing human judgment with machines. It’s about equipping auditors with smarter tools so they can focus on what matters most: protecting and enabling the organization.
As you explore the prompts above, consider how your team might customize them to your business model, control environment, and risk appetite. Then, take the next step—build a Generative AI playbook tailored to your audit methodology. In a world that rewards speed and precision, the auditors who adapt will be the ones who lead.
