The Impact Of Artificial Intelligence On Internal Audit Activities: Opportunities, Risks, And A Corporate Governance Perspective

Abstract

Digital transformation processes significantly impact the operating methods, decision-making mechanisms, and control systems of businesses, paving the way for the restructuring of corporate structures. One of the most fundamental elements of this transformation process is artificial intelligence technologies. Artificial intelligence technologies, with their wide range of applications, have a strategic and distributed structure where different technological systems work together. Considering this feature, artificial intelligence can analyze data by creating algorithms, model large and complex data sets, and produce more accurate predictions.

The main objective of this study is to investigate the impact of artificial intelligence technologies on internal audit activities in the digital transformation process; to evaluate the opportunities, risks, and effects of these technologies on corporate governance. In addition, the differences between the AI-based internal audit process and the traditional internal audit process are highlighted. Considering these factors, the future of artificial intelligence technologies and the internal audit sector is evaluated together.

P.S. The article titled "The Impact of Artificial Intelligence on Internal Audit Activities:
Opportunities, Risks and Corporate Governance Perspective" was prepared by Alp Buluç and Dr. Burak Saykal and was first published in Turkish in TİDE Academia Research, Issue 8 (2026). You can access the article via this link.

1. Introduction

The rapid developments in digital technologies in recent years have significantly changed the operational structures and organizational forms of businesses. In particular, advancements in information technologies have enabled organizations to easily access the data they need, to use this data accurately and quickly, and to positively influence decision-making processes. In this process, the concepts of digitalization and digital transformation have begun to gain importance for organizations. Digital transformation does not only refer to the use of technological tools, but also to a process that reorganizes the way organizations do business and changes the organizational culture structure (Bharadwaj et al., 2013). This process enables organizations to make faster and healthier decisions (Aslan, 2020).

One of the fundamental components of the digital transformation process is artificial intelligence technologies. Artificial intelligence, which is based on human vision, knowledge, and goals, can effectively analyze large and complex data sets, identify problems, and develop appropriate solutions (Gözbüyük, 2021). Furthermore, it can accelerate decision-making processes through predictive analysis and integrate into business processes (Berberoğlugil, 2023). Artificial intelligence technologies, which can draw upon different technologies as resources, can accurately analyze both past and present data and, based on this data, develop predictions about the future, influencing final decisions. However, it should not be forgotten that at the heart of artificial intelligence systems lies human knowledge, vision, and goals. Humans are the elements that develop algorithms and guide decision-making processes. Therefore, the goals, levels of benefit, and risks faced by parties utilizing artificial intelligence technology may differ (Ağdeniz, 2024).

The primary goal of a business is to maximize profitability, create a competitive advantage in the market, and ensure sustainable growth (Yalta & Yalta 2019). Effective risk management, a strong internal control system, and efficiently functioning governance structures are fundamental to achieving these goals. Internal audit activities emerge as a crucial factor in this perspective, meticulously examining the corporate risk management of businesses, investigating the efficiency of control processes, and ensuring their improvement. Internal audit is an independent and objective assurance activity aimed at improving business operations, increasing the effectiveness of control processes, and contributing to corporate governance (Aslan, 2020). The primary objective of this activity is to ensure that businesses achieve their goals within the specified timeframe.

Traditional audit models are defined as a process that is usually conducted over a specific period using sampling methods, is fundamentally human-based, and where analyses are carried out accordingly (Thompson, 2025). However, today, the acceleration of technological development and the increasing volume of data have led to transformations in audit processes. With the rapid development of artificial intelligence and data analytics technologies, audit processes are becoming more comprehensive, faster, and stand out in terms of helping to make accurate decisions. AI-based audit processes shorten audit times, enabling faster data analysis and the acquisition of comprehensive results. This increases the effectiveness and efficiency of audit processes. Artificial intelligence technologies can be used in different areas of the internal audit process. These mostly consist of risk analysis, data analytics, reporting, and audit planning. Thanks to this wide range of applications of artificial intelligence technologies, not only are results obtained based on past data, but also analyses based on future predictions can be performed (Berberoğlugil, 2023). In addition, the application of new audit models becomes possible with artificial intelligence technologies. These models mostly encompass studies based on real-time data analysis. These advancements in artificial intelligence technologies contribute to the internal audit function taking on a more important and strategic role for businesses. While advancements in artificial intelligence technologies, which require continuous monitoring and improvement within a cycle, are critical for businesses, they also have some disadvantages (Ghafar et al., 2024). Data security, in particular, stands out as a key factor. However, the protection of personal data and the violation of ethical principles also lead to discussions about the application of artificial intelligence technologies. In addition, the increasing importance of artificial intelligence technologies in decision-making processes highlights the need for a more careful review of the control mechanisms of these technologies. Therefore, it is critical for businesses to have a strong governance network and content in order to use artificial intelligence applications effectively and reliably within their objectives.

The main objective of this study is to investigate how artificial intelligence (AI) technologies affect internal audit activities in the digital transformation process. The study examines in detail the advantages and disadvantages of AI applications for businesses. In this context, the effects of these applications on productivity, risk management, and audit effectiveness are evaluated in detail. Furthermore, data security, ethical violations, and governance risks are also addressed in detail. In addition, the differences between the traditional audit process and the AI-assisted audit process are examined, and the future of the internal audit function and field is evaluated in the context of digital transformation.

The contribution of this study to the relevant literature is that it evaluates the effects of AI technologies on internal audit activities not only in terms of increased productivity and audit process effectiveness, but also in a multi-dimensional way from the perspectives of data governance, risk management, ethical principles, and corporate governance. While a significant portion of the existing literature on AI and internal audit focuses on the technical applications of AI or its effects on the productivity of audit processes, this study evaluates the risks and opportunities arising from these technologies in internal audit activities, and comprehensively addresses the differences between the traditional approach and the modern AI-assisted internal audit approach. Furthermore, one of the most original contributions of this study to the literature is the evaluation and emphasis of the new competencies that auditors should possess in the digital transformation process. On the other hand, in the vast majority of the relevant literature, a comprehensive framework is not found that evaluates the effects of internal audit and artificial intelligence technologies on the planning, execution, reporting, and monitoring phases of the internal audit process, as well as the governance-based risks that may arise in these processes. In this respect, the study under review aims to address this deficiency.

2. Literature Review 

Today, internal audit activities have undergone a radical transformation with technological advancements. A detailed examination of national and international literature reveals a recent increase in studies investigating the relationship between artificial intelligence and internal audit. Studies investigating the effects of artificial intelligence on businesses and decision-making processes suggest that these technologies accelerate decision-making processes, increase business efficiency, and provide companies with a competitive advantage in the internal audit process. In their study, Yardımcıoğlu & Şıtak (2020) emphasized that the acceleration of the digitalization process after the pandemic period increased the importance of using artificial intelligence in the accounting field. Wang et al. (2024) concluded that artificial intelligence technologies have a positive impact on business performance and competitiveness in the digital transformation process. Darmawati, D., Jaafar, I.N., Baja, K.H., Purisamya, J. A. & Yolanda, W.M.A. (2025) investigated the effects of artificial intelligence technologies on reporting efficiency, transparency, and accountability processes of local governments in the digital transformation process. The study results indicate that there are infrastructure deficiencies in the integration of artificial intelligence technologies in the public sector, while machine learning dominates in other processes. Studies investigating the contributions of artificial intelligence to the internal audit process show that audit effectiveness increases with the development of artificial intelligence technologies. Köse, Apalı, & Aldemir (2022) and Özyiğit (2023) stated that the development of artificial intelligence use positively affects the performance of auditors and contributes positively to audit quality. Şentürk (2023) and Demirel & Yaralı (2023) pointed out that although artificial intelligence technologies provide some benefits in audit processes, they can also bring data security and ethical risks. When examining studies on artificial intelligence, data security, and governance risks, Kolade (2024) emphasized that the use of artificial intelligence technologies should be under human supervision to strengthen the management styles of businesses, and highlighted the importance of this requirement. Karaca (2024) stated that data security and ethical issues can create critical risks in the internal audit process. In a similar study, Altınbay & Elalmış (2025) examined the relationship between artificial intelligence technologies and internal audit based on opportunities and risks. According to the study results, it was emphasized that although artificial intelligence technologies bring certain opportunities in the workflow process of internal audit processes, they also bring risks, especially in terms of ethics and data security. Kuş & Çıtak (2025) examined the impact of digitalization processes on internal audit and the role of artificial intelligence technologies in this impact. According to the study results, it was concluded that managing artificial intelligence-based internal audit processes reduces operational losses.

Studies on the future of artificial intelligence and internal audit emphasize that AI technologies will be the most important factor in the internal audit process in the future. İnce et al. (2021) stated that AI technologies will play a critical role in decision-making processes in the future. Zhou (2021) noted the necessity for internal audit activities to keep pace with technological developments in accordance with the requirements of the era, and that AI-supported internal audit processes will inevitably become more widespread. Arinola & Olalekan (2023) emphasized that auditors’ technical knowledge and skills must be provided to increase efficiency in internal audit processes. In a similar study, Leocádio et al. (2024) emphasized the importance of AI technologies on innovation, competitiveness, and corporate resilience in internal audit practices. Tekin & Demirel (2024) investigated the effects of AI on productivity and employment. According to the results obtained, it was emphasized that AI technologies may have negative effects on employment in the future. Nguyen Thi & Binh (2025) emphasized the necessity of adapting data-driven AI technologies to audit processes in the future. Shawaqfeh et al. (2026) investigated the impact of artificial intelligence on the internal audit process of commercial banks. According to the research results, it was stated that the advancement in artificial intelligence technologies has a positive impact on the internal audit process.

Studies investigating the relationship between artificial intelligence and corporate governance, such as Locke & Bird (2020), concluded that artificial intelligence technologies positively affect management practices and accelerate the decision-making mechanism. In their study, Celayir & Başar (2021) examined the accounting scandal of Olympus and analyzed the company’s deceptive accounting practices and their consequences. As a result of the study, it was stated that ethical violations and weak governance practices negatively affect businesses. Petrin (2024) drew attention to the fact that the understanding of corporate governance in companies is insufficient and needs to be re-evaluated in the digital transformation process.

3. Methodology

This study aims to investigate the effects of artificial intelligence technologies on internal audit activities. For this purpose, the conceptual analysis approach, one of the qualitative research methods, was preferred. Conceptual analysis refers to a method that aims to explain the theoretical framework related to a specific topic, to systematically evaluate and compare existing information, and to explain the relationships between related concepts (Morse et al., 1996).

The research was conducted within the scope of qualitative research methods. First, the national and international academic calendar was examined in detail within the scope of artificial intelligence technologies, digital transformation, risk management, and data governance. Then, suitable sources were identified and addressed according to the content. Subsequently, the effects of artificial intelligence technologies on the internal audit process, data analytics, risk management, data security, and ethical principles were classified in a content-oriented manner. Finally, the relationship between artificial intelligence technologies and internal audit activities, along with these contents, was investigated as a whole within the framework of the opportunities created, the risks that may arise, and corporate governance. Basically, the data source of the research consists of national and international academic literature related to artificial intelligence. In the literature research process; The study extensively utilized resources from Google Scholar, Web of Science, Dergi Park, and Science Direct. Key keywords used in this process included artificial intelligence, internal audit processes, digital transformation, AI and technological advancement, and AI and data governance. Several criteria were considered in selecting the sources included in the study. First, sources researching artificial intelligence technologies were examined in detail, focusing on recently published studies to reflect the topicality of the subject. Then, studies investigating internal audit and artificial intelligence technologies together were reviewed. Furthermore, studies addressing technological advancement and digital transformation were examined. Finally, current studies focusing on data analytics, risk management, and AI ethics were included in the research scope. In addition, studies examining the perspective of artificial intelligence and corporate governance were focused on. Exclusion criteria included studies unrelated to the topic. Studies outside the peer-review process in academic journals were also excluded. Furthermore, studies that did not link artificial intelligence technologies to the internal audit process were disregarded. Academic studies used as sources throughout the research process were evaluated through content analysis. Content analysis is an analytical technique that enables the systematic classification, categorization, and interpretation of data related to a specific topic, both quantitatively and objectively (Krippendorff, 2018).

The studies in question are classified under the following headings: the effects and consequences of artificial intelligence on the internal audit process, the importance of data analytics and its effects on risk management, ethics and data security, assumptions about the future of the internal audit process, and the corporate governance perspective. In the final section, the findings obtained in the study are evaluated comparatively, and the effects of artificial intelligence technologies on internal audit activities are discussed comprehensively. This study differs from the relevant literature by evaluating the positive and negative effects of artificial intelligence technologies on the internal audit process from a holistic perspective. In doing so, it comprehensively addresses the risks that may arise from artificial intelligence technologies in internal audit processes and the possible effects of these risks on the functioning of internal audit activities and process management. In this respect, it provides a theoretical contribution to the relevant literature. In addition, internal audit processes are not only examined on the basis of efficiency and technological development, but also in conjunction with data governance and ethical principles. In addition, it differs from existing studies because it investigates what competencies internal auditors should possess in the process of technological progress and transformation and the future of the internal audit profession. To enhance the academic validity of the study, only academically peer-reviewed national and international publications were selected as data sources. In this phase, studies indexed in international databases were examined comparatively. Different sources yielding similar results were utilized. To ensure the academic reliability of the study, the source selection, analysis method, and classification processes determined before the study were taken as a basis. The study results were reinterpreted by comparing them with the relevant literature.

4. Digitalization and Artificial Intelligence

The concepts of digitalization and digital transformation are often used interchangeably. However, these two concepts differ significantly in scope and content. At the heart of digitalization lies the transfer of data from a physical environment to a digital environment and the systematic use of that data. This allows data to be stored more easily and accessed quickly when needed. This significantly simplifies data analysis processes. On the other hand, digital transformation is not simply about transferring data to a digital environment.

Digital transformation refers to an integrated change in the operational processes, business models, and organizational structures of businesses, along with the data transferred to the digital environment, and a planned and more directional change process that arises as a result of this change. In this context, digital transformation is not only a result of technological progress but also represents a cultural, administrative, and organizational transformation process for businesses (Alina, 2024).

Among the technologies that businesses generally utilize in the digital transformation process are: These technologies include data analytics, machine learning, cloud computing, robotic process automation (RPA), the Internet of Things (IoT), enterprise resource planning (ERP) systems, and artificial intelligence. These technologies are frequently preferred by businesses because they enable cost minimization, operational efficiency, and faster decision-making processes. Data analytics allows for the accurate and precise analysis of large and comprehensive data. Machine learning algorithms allow for detailed utilization of historical data (Arslan, 2020), enabling systems to make accurate predictions for the future (Berberlioğlu, 2023). Cloud computing, particularly for small and medium-sized enterprises (SMEs), offers data storage and flexible processing capacity, providing cost flexibility for such businesses (Cesur, 2025). Robotic process automation (RPA) automates repetitive, control-based, and rule-based work models to minimize human error. RPA is also defined as increasing the added value of the workforce. The Internet of Things (IoT) creates significant advantages for businesses, especially in logistics and production processes, by enabling physical devices to communicate with each other and with centralized systems. ERP systems, on the other hand, create an integrated management structure by integrating the core functions of businesses such as finance, production, workflow models, human resources, and supply chain. Today, artificial intelligence technologies stand out as one of the most decisive factors for businesses in the digital transformation process (Dwivedi et al., 2021). At the heart of artificial intelligence lies human vision, knowledge, and goals. In this respect, the human factor should not be ignored in the production, management, and final decision-making stages of algorithms (UNESCO, 2021). Although fundamentally based on the human factor, artificial intelligence technologies also have a structure that works interactively with different technological systems. When evaluated with these characteristics, artificial intelligence technologies, which can be guided by a human perspective, can produce different algorithms by working with different systems and use data in this way (Koçyiğit & Darı 2022).

Artificial intelligence technologies can be used as a sub-component of other technological applications (Öztemel, 2020). Furthermore, artificial intelligence technologies can analyze large datasets in detail (O’Malle, 2014). Businesses can analyze both past and current data and policies. As a result of these analyses, they can make reliable predictions and forecasts for the future.

Artificial intelligence technologies can be used by businesses for various purposes and have an impact on decision-making mechanisms (Berberlioğlu, 2023). While traditional decision-making processes generally rely on managerial experience and limited data access, AI-assisted decision-making processes allow access to more comprehensive and objective data. For example, businesses can accurately assess potential risks, predict customer expectations, and generate alternative policies to gain significant advantages. In addition, artificial intelligence applications are transforming internal audit processes (Alina, 2024). Thanks to approaches such as continuous auditing and monitoring, processes can be analyzed in real-time, and potential risks can be identified at an early stage. These approaches contribute to businesses operating their control mechanisms more effectively and preventing potential errors or abuses. In conclusion, digitalization represents a starting point for businesses to use their policies more effectively and efficiently in line with their goals, while the digital transformation process refers to the integrated implementation of these goal-oriented policies. Artificial intelligence technologies, at the core of this transformation process, not only provide businesses with operational efficiency but also create a competitive advantage in their market. In this context, digital transformation has become a necessity for businesses that are currently implementing policies in line with these goals.

5. Artificial Intelligence, Data Governance, and Corporate Governance

Data governance refers to the management of the processes of processing and using data collected by artificial intelligence applications within the framework of ethical and legal principles (Ağdeniz, 2024). Artificial intelligence works with data. It processes this data with algorithms and obtains results (O’Malley, 2014). Therefore, it is of great importance that the information obtained from users is processed and used in accordance with ethical and legal principles on the basis of data governance. At this point, questions such as what data is preferred, which data is more important, who owns the ownership rights of the data, and how the data should be protected come to the fore. Considering current practices, it is understood that there are many legal gaps in the field of data governance. Many users knowingly or unknowingly consent to the use of their data. This situation especially leads to the creation of security vulnerabilities. For data governance to be effectively implemented, data must be obtained and used completely and accurately. Despite rapid advancements in artificial intelligence technologies, human-specific emotions and thoughts such as creativity, critical thinking, and ethical values ​​still retain their importance. Therefore, cleaning and structuring data during the analysis phase is still critically important for achieving the desired outcomes (O’Malley, 2014). Corporate governance is the system by which organizations are directed, managed, and controlled, overseeing the reciprocal relationship between the board of directors and other stakeholders (Aladağ, 2024). Considering today’s business environment, strong corporate governance is critically important for companies (Tallarita, 2023). This importance manifests itself in areas such as decision-making mechanisms, risk management, accountability, and transparency. These challenges lead to changes in the expectations of many stakeholders, including the board of directors. Therefore, corporate governance needs to be strong in the face of changing expectations. This strong structure emerges within an accountable, adaptable, flexible organizational system that can effectively manage risks and expectations (Bari, 2024). From a corporate governance perspective, the increasing use of artificial intelligence technologies in business processes brings about new responsibilities for internal audit units (Tallarita, 2023). For artificial intelligence technologies to be accountable, they must be transparent, traceable, and explainable (UNESCO, 2021). In addition, data security, ethical use, and the auditability of algorithmic decisions are among the basic requirements of the corporate governance structure (AI Governance Alliance, 2024). Artificial intelligence technologies positively influence decision-making mechanisms in corporate governance (Mertens, 2023). This impact is evident in the transformation of corporate governance capacity. Artificial intelligence technologies, with their advanced data processing capabilities, can easily distinguish complex data and derive meaningful results (Mertens, 2023). This allows companies to use decision-making processes more effectively and quickly on behalf of boards of directors and decision-makers (Gözbüyük, 2021). Accountability and transparency are considered important trust factors in corporate governance (Aladağ, 2024). Artificial intelligence technologies can identify any errors that may occur in manually obtained data during the analysis phase. Thus, they prevent potential risks and ensure reliability. This helps create accountability and transparency in companies. Artificial intelligence technologies provide various benefits to companies in terms of risk management within corporate governance. In traditional risk management processes, the probability of human-induced errors is quite high. However, in AI-assisted risk management, potential risks are monitored and evaluated autonomously. This allows companies to minimize their risks.

6. The Use of Artificial Intelligence in Internal Auditing

Among the main objectives of internal audit activities are increasing the effectiveness of control mechanisms in businesses and strengthening the corporate governance structure (Bari, 2024). In this context, artificial intelligence technologies contribute to the application of corporate governance principles in businesses by enabling more effective monitoring of risk management and management processes. Internal audit is an independent and objective assurance and consulting activity aimed at improving the operations of businesses and adding value to them (Sabuncu, 2017). In this context, internal audit provides a systematic and disciplined approach aimed at evaluating and improving the effectiveness of risk management, control, and governance processes of businesses (Schuet, 2025). In internal audit, the concept of corporate trust is one of the most fundamental elements for businesses to effectively implement their policies and ensure sustainable growth. For this purpose, the effective and accurate integration of artificial intelligence technologies used by businesses into audit processes plays a critical role in strengthening corporate trust (Ismail et al., 2024). Therefore, the transparency, accountability, controllability, and ethical compliance of AI-based audit processes are crucial for establishing and maintaining corporate trust in businesses (Ağdeniz, 2024). The internal audit process fundamentally consists of four basic stages: planning, execution, reporting, and monitoring. In today’s context, thanks to artificial intelligence and transformation technologies, these stages can be implemented in an integrated manner (Ghafar et al., 2024). Thanks to this integrated structure, the internal audit process has become faster, more comprehensive, and more data-driven. It is observed that there are significant differences among audit tools depending on the advancement of technology (Munoko et al., 2020). Traditional audit tools are based on methods such as substantive accuracy tests, analytical procedures, and control tests. On the other hand, technological audit tools are based on computer-aided data analytics software and artificial intelligence technologies.

6.1 Risk Analysis

Artificial intelligence technologies can quickly analyze large-scale data. They also have the capacity to obtain accurate results from these datasets, thus identifying potential risks (Mertens, 2023). Unlike traditional methods, AI technologies utilize machine learning algorithms. This allows them to analyze historical data and utilize advanced techniques such as recognizing new and different data, anomaly detection, and trend analysis (Öztemel, 2020). Thanks to these features, they can detect unusual transactions and processes (Schuet, 2025). Furthermore, the impact and consequences of identified risks can be analyzed in detail, and a prioritization process can be established (Alina, 2024). This saves time and data for auditors, enabling more effective use of resources. Additionally, their real-time data processing capabilities for dynamic risk monitoring make the audit process more proactive (Ghafar et al., 2024).

6.2 Data Analytics

The ability of artificial intelligence technologies to analyze and detail data of varying sizes at high speed and accurately is critically important for identifying discrepancies. In this process, working with large and complex data using traditional systems increases the likelihood of human-induced errors. Failure to detect these errors leads to inaccurate results and unexpected deviations (Kontogeorgis, 2025). At this stage, artificial intelligence applications, thanks to their advanced algorithms in the analysis phase, more effectively identify errors and inconsistencies in datasets, while ensuring reliable results. Thus, they contribute to the identification of potential control vulnerabilities.

AI applications can detect recurring data and patterns, or data structures that change over the long term, through machine learning and time series analysis (Munoko et al., 2020). This detection allows businesses to analyze cost structures, sudden fluctuations in income-expense balance, and performance dynamics in detail, leading to clearer analyses and the development of healthier future predictions.

Finally; To counter the inconsistencies caused by differing sources and methods, artificial intelligence technologies can prevent such discrepancies through their comparison and matching techniques. In this context, AI applications not only ensure fast and accurate results in the data analysis process but also refine the scope and depth of the obtained data.

6.3 Continuous Monitoring

In traditional audit processes, data analysis is typically performed over a specific time period. In contrast, AI-powered audit processes allow for data analysis over a much broader timeframe. Furthermore, in AI-based audit processes, operations can be carried out continuously and simultaneously (Ismail et al., (2024). Thanks to these features, instant solutions can be generated for any negative situations that businesses may encounter. Thus, the audit process becomes more proactive rather than reactive. Continuous auditing is particularly important for large-scale companies in terms of increasing the effectiveness and efficiency of control mechanisms and helping to minimize risks (Munoko et al., 2020).

In addition, since AI applications can also benefit from past data in audit processes, they can make comparisons between the results obtained. The results can be reviewed again, and more rational solutions and policy recommendations can be offered for the future. This allows auditors to move beyond the limitations of classical audit processes where they only evaluate the current situation, enabling them to assume a more strategic auditor role.

In conclusion, the continuous audit system offers significant contributions to businesses in terms of reliability, transparency, and institutionalization. In this respect, it stands out as one of the most fundamental parts of the contemporary audit understanding.

7. Differences Between AI-Powered Auditing and Traditional Auditing

The fundamental differences between AI-assisted auditing and traditional auditing lie in the scope and duration of the audit process, the preferred methods, and the effectiveness and efficiency levels of the results obtained.

Traditional audit processes refer to a process based on a specific period and generally performed manually. In this audit process, time, resources, and data sets are limited. In this process, mostly previously occurred events are examined. Therefore, the traditional audit process is, in a sense, a reactive process. This situation generally makes it possible to identify risks and uncertainties only after the event has occurred. In this context, the decision-making processes for analysis and audit results are prolonged. Traditional audit processes generally conduct their studies based on past data, and their sampling methods also rely on past periods (Kaya, 2024).

In contrast, audit processes based on artificial intelligence and other digital technologies can monitor data flows in real-time thanks to their advanced technological infrastructure. Furthermore, they can provide results and recommendations. Thanks to the benefits provided by these systems, data analysis processes can be performed programmatically. At the same time, in AI-assisted audit processes, analyses performed with large datasets can be carried out quickly and with high accuracy rates. In studies conducted, the ability to analyze all data instead of just a sample allows for a broader scope of auditing (Munoko et al., 2024). Furthermore, the automated programming capabilities of artificial intelligence technologies enable the detection of errors and irregularities. Because AI-supported audit processes have the ability to learn from past data, they can generate more accurate predictions and forecasts for the future. This generally helps prevent potential risks.

Overall, the AI-supported transformation process significantly improves audit activities, making them faster, more effective, and yielding quicker results (Thompson, 2025). However, taking necessary security measures is critically important (Gyevnar & Kasirzadeh 2025).

Table 7.1 summarizes a comparison of the traditional internal audit process with the AI-supported internal audit process. 

Table 7.1 Comparison of Traditional Internal Audit and AI-Assisted Internal Audit

Source: Created by the authors using materials from Munoko et al. (2020), Ismail et al. (2024), and Wassie & Lakatos (2024).

Analysis of Table 7.1 reveals that AI-based internal audit activities offer significant advantages over the traditional internal audit approach in terms of audit speed, data processing capacity and duration, and the speed of the audit process. However, it also appears that AI-assisted internal audit activities introduce several new risks, such as data security, algorithmic bias, and governance risks.

7.1. Essential Competencies for Internal Auditors in the Age of Artificial Intelligence

Today, the rapid integration of artificial intelligence technologies into business processes has led to radical changes in the methods and procedures applied in internal auditing. In this context, professionals involved in audit processes need to possess sufficient technological knowledge and skills in conjunction with digital transformation. The effective and efficient use of technological tools is considered one of the fundamental elements that increase the quality and effectiveness of audit activities (Wassie & Lakatos, 2024). Accordingly, analytical thinking skills and the ability to perform data-driven analyses in digital environments are indispensable requirements of the internal auditing profession (Ismail et al., 2024). From this perspective, data analytics has become one of the most fundamental competencies for companies conducting internal audits. Data analytics plays a crucial role in accurately analyzing data sets and identifying problems. In this context, basic statistical knowledge is paramount. Concepts such as probability, distribution, regression, and correlation, which are part of basic statistics, ensure that the results obtained from internal audits are based on a more solid foundation and that the analysis results are more reliable.

The increasing use of artificial intelligence technologies has led to a rise in the importance of data security and data governance issues (Berberlioğlu, 2023). In this context, protecting the data held by institutions and organizations, limiting unauthorized access, and ensuring that the implemented procedure complies with the legislation are within the responsibility of auditors. Therefore, it is crucial for businesses and professionals conducting internal audits to possess the necessary knowledge and competencies in data privacy and cybersecurity. Awareness created regarding the protection of personal data and ethical data use plays a key role in achieving sound results in the audit process. Understanding the fundamental working systems of machine learning and artificial intelligence technologies, which mean that algorithms analyze past data in detail to derive predictions and conclusions for the future, has become an important area of ​​competence in the internal audit process (Wassie & Lakatos, 2024). However, internal auditors need to develop and advance these systems at a technical level. The fundamental expectation from internal auditors is: Understanding how the algorithms used work, how the dataset is utilized, the source of the data, and how to identify potential errors and incorrect results is crucial. This understanding is vital for ensuring the accuracy of artificial intelligence applications and the robustness of the results obtained during the audit process.

In the audit process involving artificial intelligence technologies, ensuring that decision-making mechanisms comply with the principles of transparency and accountability has become a significant responsibility of the audit function. Careful analysis of potential ethical risks and uncertainties in audit processes has become a crucial area for both the companies and professionals conducting the audit (Aslan, 2010).

In conclusion, the active involvement of artificial intelligence technologies in the internal audit process today has transformed it into a multi-dimensional structure integrating technical knowledge and analytical thinking. For businesses and employees to adapt to this audit process, they need to be open to continuous learning and development (Aslan, 2010). Alongside this requirement, discipline is also crucial for the effective progress of the process. These competencies not only increase the effectiveness of audit processes but also constitute a fundamental element of sustainable growth for businesses.

8. Conclusion

The integration of artificial intelligence technologies into the business world is causing profound changes in the operational processes of internal audit firms. This transformation in the integration process allows for a more comprehensive approach to audit activities and the achievement of healthier results. While the traditional internal audit approach is based on the human factor, can be worked on in specific periods, and has a sampling model, the modern internal audit approach based on artificial intelligence is based on digital transformation, works with big data, has continuous monitoring capacity, and can predict risks in advance. This study comprehensively examines the effects of artificial intelligence technologies on the internal audit process. In this context, the opportunities created, the risks, and corporate governance perspectives are the main topics discussed. The relevant literature has been reviewed in detail, and it has been observed that artificial intelligence technologies generally have a positive impact on internal audit activities. In particular, increased efficiency, reduction of error rates, improvement of data analytics, and risk management are noteworthy advantages that artificial intelligence technologies have brought to internal audit activities. Thanks to these advantages, audit activities are carried out from a broader perspective and acquire a proactive character. Although artificial intelligence technologies positively impact audit activities, they also bring certain risks. In this process, issues such as the protection of personal data, data reliability, transparency, and accountability are becoming critically important areas for businesses. Furthermore, decision-makers need to utilize artificial intelligence technologies with full competence in the audit process. These competencies include statistical information, data analytics, and data governance, which are fundamental requirements. A lack of this competence leads to negative impacts on the workflow processes of audit activities. This context is significant because it shows that the internal audit process will transform into a more technologically advanced structure in the future. In conclusion, artificial intelligence technologies currently increase the effectiveness of internal audit activities. In addition, they positively impact audit processes and make significant contributions to corporate governance. For these technologies to be utilized effectively, data security and ethical principles must be considered. Within this framework, businesses need to ensure accountability and transparency, and develop strong governance mechanisms. With technological advancements, it is predicted that artificial intelligence technologies will be used more frequently in internal audit activities, and it is important for businesses to adapt to technological innovations within the framework of their corporate responsibilities.

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