By: Durran Dunn | 01/29/26
The New Era of AI in Internal Auditing
The internal audit profession has always been a quiet witness to the habits and blind spots of organizations. For decades, the work relied on manual sampling, interviews, and the kind of spreadsheet artistry that only auditors and magicians truly appreciate. The landscape has shifted. Automation, cloud systems, and structured digital workflows have matured. Today the acceleration of AI in internal audit is happening because organizations need real time clarity on risks that used to surface only after year end or after the system had already broken.
AI is now a strategic priority. Boards and executive teams want stronger data integrity. They want continuous monitoring of Financial Internal Controls. They want earlier fraud detection and fewer moments where someone in a conference room says, I wish we had caught that six months ago. Regulatory expectations around reporting accuracy and cybersecurity monitoring are also rising. With a more complex operating environment, organizations are looking for tools that can scale faster than internal teams and push assurance capabilities further. AI answers those needs.
The adoption curve is steep because internal audit functions now operate in an environment that produces an immense volume of structured and unstructured data. AI brings the speed to digest that information at scale. More importantly, it brings pattern recognition and adaptive learning that move beyond what spreadsheets and traditional scripts can reveal. The result is an emerging model of real time assurance that changes how organizations understand risk.
When applied with discipline, AI improves accuracy and reduces the number of hours auditors spend combing through repetitive detail. The technology does not get tired, distracted, or influenced by the quality of the conference room coffee. Predictive models can analyze transactions and identify patterns that traditionally required manual review across multiple systems. This is one of the core reasons data analytics in internal audit continues to grow in importance.
One of the most powerful applications is anomaly detection. AI models review entire transaction populations rather than small samples. They identify unusual combinations of attributes such as vendor changes, payment timing anomalies, duplicate invoice structures, or inconsistent approval paths. This helps auditors see issues before they escalate and improves the overall reliability of the Internal Control Framework.
Predictive analytics takes this further. Instead of simply flagging historic anomalies, predictive models identify future risk likelihood. These insights support better planning, more focused fieldwork, and more targeted testing. They also support areas that rely heavily on data analysis for internal auditors such as procurement, revenue assurance, treasury functions, and payroll reviews.
Natural language processing is another growing area. Internal audit teams receive mountains of documentation. Emails, policies, contracts, and procedural guides all contain clues about control breakdowns. Natural language models categorize information, identify inconsistencies, and surface documents that require auditor review. This gives teams more time to evaluate issues instead of sorting through them.
Process mining visualizes how a workflow operates in real life. Every data point becomes a breadcrumb that reveals system paths, exceptions, and bottlenecks. AI tools compare those real paths to intended policies and flag deviations. When used correctly, process mining shortens audit cycles and increases the accuracy of operational reviews.
Together these capabilities provide an ai overview audits teams can use for measurable outcomes. Organizations often see reductions in audit hours for repetitive testing. They see improved accuracy in exception identification. They benefit from a more comprehensive view of risk that traditional approaches struggle to achieve. The growth of ai in auditing is not about replacing auditors. It is about building a function that can move faster and reach deeper than before.
The next challenge is integration. AI works best when paired with clean data, well defined processes, and collaboration between internal audit and IT teams. The technology is sophisticated, but the principles are straightforward.
Start with data readiness. AI models rely on consistent, accessible, and high quality information. Internal audit teams should perform a readiness review that examines data sources, data quality standards, and documentation of business rules. This ensures that the models are using information that reflects the actual behavior of the organization.
Collaboration with IT is essential. Internal audit may own the analytical outcomes, but IT owns the security, governance, and underlying systems. Successful AI implementations require clear roles, documented decision points, and joint understanding of system interdependencies. These partnerships help prevent common pitfalls such as uncontrolled data extracts, outdated system mappings, or unmonitored model behavior.
Change management is the third pillar. Many organizations underestimate the cultural shift required to introduce AI into audit testing. People are comfortable with their existing methods. They trust their sampling techniques. They trust their intuition. AI changes the work. It introduces new workflows and a different balance between manual and automated analysis. Teams need training, communication, and clarity about how the technology supports, rather than replaces, their judgment.
This is where strong advisory guidance matters. BT’s Risk Advisory Services and technical teams have supported organizations that want to align AI capabilities with existing audit frameworks. Sometimes the work is about reshaping the methodology. Sometimes it is about developing controls around model outputs. Sometimes it requires helping clients understand how Outsourced Accounting Solutions, SOC Reporting Services, or IT reviews intersect with AI driven analytics. The technology becomes far more powerful when integrated thoughtfully into the broader control environment.
AI does many things well. It also introduces new questions. Accountability, model bias, data privacy, and transparency all rise to the surface as organizations rely more heavily on intelligent tools.
Accountability starts with understanding the model itself. Internal auditors need to know what the AI system is designed to do, what data it uses, how frequently it updates, and where its limitations exist. When auditors develop procedures for solutions for continuous ai system performance auditing, they create safeguards that ensure the technology performs consistently and predictably.
Bias is another major topic. If the data that trains the model contains historical bias, the model may reproduce that bias in its results. Internal auditors must validate training data, review model outputs, and challenge anomalies that do not align with business logic.
Privacy protections are critical. AI systems often ingest sensitive data, including financial transactions, payroll details, asset inventories, and operational metrics. Auditors need assurances that privacy requirements are met and that access is controlled. This includes reviewing data storage, encryption, and retention policies.
Transparency remains a challenge because many AI models operate as complex black boxes. Internal auditors must determine how to evaluate results when the algorithm itself is not fully visible. This typically involves testing outputs, validating calculations, and challenging inconsistent behavior. It also includes monitoring compliance with evolving standards from regulators and public sector bodies such as the Government Accountability Office.
Effective governance ensures that AI is not only powerful but trustworthy. The strongest AI enabled audit functions understand the technology and know how to challenge it appropriately.
AI is reshaping the role of internal auditors. Traditional audit work focused on retrospective assessments. Today the profession moves steadily toward proactive insight. Continuous monitoring, real time alerting, and predictive modeling allow auditors to anticipate issues before they turn into breakdowns.
This shift requires new skills and updated competencies. Internal auditors need a working understanding of AI capabilities. They need comfort with data literacy. They need to collaborate more frequently with data engineers, developers, cybersecurity teams, and operational leaders. The future ready internal audit function works across disciplines, not in isolation.
Training and upskilling matter. Organizations should build programs that support technical training, control testing enhancements, and analytical reasoning. Teams that learn how to interpret model outputs, validate logic, and tell the story behind the numbers will have a competitive advantage.
Cross functional integration strengthens the value of AI. Audit insights should inform operational improvements, compliance functions, financial reporting teams, and strategic planning groups. When organizations treat internal audit as a strategic partner, AI becomes a tool for enterprise level value rather than a standalone experiment.
Over time AI will help internal auditors focus on judgment driven tasks. Evaluating management intent, investigating root causes, designing stronger controls, and communicating findings all require human skill. AI supports the work by accelerating the analysis and giving teams more time for interpretation.
The internal audit function of the future is not just tech enabled. It is more agile, more informed, and more connected to the organization’s long term goals.
Internal auditors need comfort with data literacy, an understanding of how models operate, and the ability to validate outputs. Skills in critical thinking and risk assessment remain essential. Auditors should learn enough about AI to challenge results and ensure alignment with business logic and control frameworks.
AI strengthens objectivity by reducing manual bias in sampling and review. Independence remains grounded in the auditor’s role, not the tool. As long as auditors validate the model, review outputs critically, and maintain control over conclusions, AI supports rather than compromises independence.
Yes. AI reviews large volumes of transactional data, identifies unusual combinations of variables, and highlights patterns that are difficult to detect manually. This includes timing anomalies, behavioral irregularities, and hidden relationships between entities. AI does not replace investigator judgment but enhances detection capabilities.
Organizations should evaluate reductions in audit hours, improvements in accuracy, earlier detection of control breakdowns, and lower compliance risk. ROI also includes avoided costs such as fraud losses or remediation expenses. The value grows as models improve and integrate more deeply into monitoring activities.
Common risks include poor data quality, inadequate governance, unclear model documentation, and lack of change management. Teams may also underestimate privacy considerations or fail to monitor model drift. Strong oversight, testing protocols, and cross functional collaboration reduce these risks and help ensure predictable performance.
Durran Dunn
Bennett Thrasher LLP
Phone: (770) 396-2200
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