In our previous discussion on what sets apart exceptional internal auditors, we highlighted two foundational competencies: searching and verification. While standard audit practices often focus on compliance testing within established procedures, this approach alone may fall short in uncovering deeper risks or irregularities. It is the initiative to go beyond the checklist, through independent verification and critical investigation, that distinguishes high-performing auditors from the rest.
Beyond Compliance: A Deeper Approach to Procurement Audits
Consider the case of procurement audits. A typical audit may find no issues if all procedural steps—such as monetary approvals and interdepartmental authorizations—are properly followed. However, this procedural compliance does not always reflect the substance and integrity of the underlying transactions.
An outstanding internal auditor will seek additional context. For example, when reviewing vendor selection, the auditor may consult:
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Government business registration databases to verify vendor legitimacy.
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Director registries or public company filings to identify relationships between vendors and internal stakeholders.
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Professional licensing bodies to ensure vendors meet legal and technical qualifications.
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Public news sources or regulatory actions that could raise concerns about vendor reputations.
By triangulating multiple data points, the auditor brings greater depth and assurance to the audit process
The Challenge of AI-Generated Content
As artificial intelligence (AI) advances, internal auditors face a new and evolving challenge: the reliability of visual audit evidence. AI-generated images are becoming increasingly realistic, and may be inadvertently—or intentionally—used in documentation, marketing, or reports. Auditors must therefore develop the ability to detect AI-generated visual content to preserve the integrity of their assessments.
Techniques for Verifying Visual Evidence
- Visual Cues and Anomalies: AI-generated images often contain tell-tale distortions, especially in areas such as human hands, facial expressions, and symmetry. These details can help auditors identify inauthentic images.
- Textual Clues: AI tools frequently fail to reproduce coherent text within images. Blurry or nonsensical text is a strong indicator of AI manipulation
- Contextual Consistency: Review images for logical consistency—for example, mismatched lighting, incorrect placement of objects, or implausible environments.
Technology Aids:
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Tools like Sightengine can help flag AI-generated visuals.
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Digital watermarking technologies such as Google SynthID embed invisible identifiers that indicate whether an image has been AI-generated.
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Implications for Internal Auditing
The rise of generative AI has serious implications for audit evidence evaluation, fraud detection, and governance assurance. In this new environment, internal auditors must:
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Apply critical thinking to all forms of evidence, especially visual and digital documentation.
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Stay informed about emerging technologies that impact audit reliability.
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Balance innovation with a strong ethical lens, ensuring that decisions are grounded in verifiable and trustworthy information.
Understanding the limitations of AI—such as visual inaccuracies or anatomical inconsistencies—empowers auditors to maintain high standards of due diligence and objectivity.
Conclusion
As the digital landscape evolves, so must the internal audit profession. By enhancing traditional audit practices with critical analysis, technological awareness, and investigative rigor, auditors can continue to deliver assurance in an increasingly complex and AI-influenced world.
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