Artificial intelligence is rewriting the rules of decision-making and data analysis and internal auditors are at a crossroads. The once-static tools of audit programs, spreadsheets, and retrospective interviews are no longer sufficient. Increasingly, internal audit teams are being asked to deliver insights—not just assessments. And to do that at scale, speed, and accuracy, they need a new skill: prompt engineering.
Prompt engineering—the practice of crafting effective instructions or “prompts” to elicit high-quality responses from generative AI models—is quickly becoming a must-have capability for modern audit professionals. For those working with complex systems, unstructured datasets, or enterprise risk environments, knowing how to ask the right question is just as critical as knowing what to ask.
This isn’t a future trend. It’s already here. And it’s transforming how internal auditors write risk summaries, identify anomalies, and interrogate vast troves of operational, compliance, and financial data.
From Risk Assessments to Intelligent Dialogue
Traditional internal audit has long relied on structured templates, manual reviews, and siloed systems. However, modern GRC platforms are now starting to incorporate embedded generative AI, enabling real-time analysis, document summarization, and natural language querying. But simply having AI at your fingertips isn’t enough. The value lies in how you communicate with it.
Consider the following two prompts:
- Poor Prompt: “Summarize this policy.”
- Effective Prompt: “Review this third-party risk policy and highlight any gaps in control coverage relative to ISO 27001. Summarize findings in three bullet points and recommend mitigations.”
The difference in outcome is night and day. One gives you a general overview. The other delivers insight with strategic alignment.
Why Prompt Engineering Matters in Audit
Prompt engineering empowers auditors to:
- Accelerate control testing by querying large datasets for exceptions or anomalies
- Draft risk summaries aligned to frameworks like COSO or NIST in minutes
- Uncover red flags by surfacing contradictory or missing entries across systems
- Automate preliminary findings that can be reviewed and refined by human experts
It’s not about replacing judgment—it’s about enhancing it.
Case Example: Prompt Engineering in Action
Let’s walk through a practical scenario.
Use Case: An auditor is reviewing third-party vendors for signs of non-compliance with cybersecurity protocols. They’ve uploaded several SOC 2 reports, a recent third-party breach disclosure, and internal risk assessments into the platform.
Prompt 1: Document Analysis
“Read the attached SOC 2 reports and identify any control gaps related to incident response or data encryption. Cross-reference with my organization’s internal cybersecurity risk matrix.”
Result: The system returns a list of vendors with gaps, notes whether encryption protocols meet internal thresholds, and flags one vendor as high-risk based on lack of defined incident response protocols.
Prompt 2: Risk Summary Drafting
“Generate a risk summary memo for executive leadership that outlines the exposure presented by Vendor X, quantifies potential impact based on internal thresholds, and suggests immediate and long-term mitigation strategies.”
Result: Within seconds, the auditor has a concise, professional-grade summary that can be tailored for board or committee meetings.
Prompt 3: Audit Trail Creation
“List the steps taken in this review, including source documents referenced, filters applied, and dates of interaction, in a format suitable for audit evidence documentation.”
Result: A ready-to-export audit trail is generated, complete with time stamps, source links, and rationale for decisions—automating what would normally take hours.
Building the Skillset: A Framework for Prompting
Just as auditors are trained to interview stakeholders and interpret evidence, they now need to learn how to “interview” their AI systems. Here’s a basic framework for effective prompts:
- Objective – Clearly define what you want the AI to do (e.g., summarize, compare, list, flag).
- Context – Provide necessary background or link to documents/data.
- Constraints – Specify format, tone, length, or framework.
- Expected Output – Describe what a “good” result looks like.
Example:
“Analyze this 2024 enterprise risk register and highlight the top five operational risks by likelihood and impact, using a 5×5 risk matrix. Format as a table.”
A New Role for Internal Audit
As audit committees and regulators increasingly demand more timely, forward-looking insight, the internal audit function is evolving into a more strategic partner. This transformation requires not just technical proficiency, but also data fluency and AI literacy.
Prompt engineering is more than just a productivity hack—it’s a way of thinking. It’s the bridge between data abundance and actionable insight.
At Empowered, we’re embedding these capabilities into our GRC platform through ConnectedIQ, not just because AI is trending, but because the future of risk, compliance, and internal control depends on intelligent augmentation.
Conclusion: The Next Generation of Auditors
Prompt engineering doesn’t replace traditional audit skills—it builds upon them. Internal auditors already know how to ask tough questions. Now, they need to learn how to ask those questions in a way that generative AI can understand and answer with precision.
As AI continues to reshape the corporate landscape, the most effective internal auditors will be those who master both the human and machine elements of inquiry.
The future of audit isn’t automated. It’s augmented.
