Editor’s Introduction Edition 1, Q1, 2026

The Internal Audit Review has been created at a time when the internal audit profession is undergoing profound change. Expectations placed on internal auditors have expanded rapidly, while the risk landscape has grown more complex, interconnected, and less predictable. Traditional models of assurance, while still essential, are no longer sufficient on their own. Internal audit is increasingly expected to provide insight, foresight, and perspective — to help organisations navigate uncertainty rather than merely confirm compliance.

This publication exists to support that evolution.

Why Internal Audit Review, and Why Now

Across industries and sectors, internal audit functions are being asked to do more with less, to cover broader risk universes, and to operate at greater strategic altitude. Digital transformation, cyber risk, regulatory change, data integrity, environmental and social responsibility, and geopolitical volatility have all reshaped the assurance agenda. At the same time, audit committees and executive management expect internal audit to be relevant, timely, and forward-looking.

Yet many practitioners experience this shift in isolation — grappling with new expectations without always having access to practical insight, peer learning, or space for thoughtful reflection. Internal Audit Review was conceived in response to that gap.

This journal is intended to be a place where ideas can be explored with depth, where emerging challenges can be examined critically, and where professional judgement is valued as much as technical compliance. It is not designed to replicate standards or restate guidance already available elsewhere. Instead, it seeks to complement them by focusing on interpretation, application, and lived experience within the profession.

A Quarterly Space for Reflection and Insight

As a quarterly publication, Internal Audit Review is deliberately paced. In a world saturated with rapid commentary and fleeting opinion, there remains a need for considered analysis — writing that allows time to reflect, connect themes, and extract meaning.

Each edition will focus on issues shaping the present and future of internal audit, drawing on contributions from practitioners, leaders, and subject-matter experts. Articles will range from strategic perspectives and thematic analysis to practical insights grounded in real-world audit environments. Over time, the Review aims to build a body of work that reflects the maturity, diversity, and evolving nature of the profession.

This first edition sets the tone. It marks the beginning of an ongoing conversation — one that will develop, deepen, and broaden with each subsequent issue.

Beyond a Journal: An Evolving Initiative

While Internal Audit Review takes shape initially as a publication, it represents only one part of a broader initiative.

As we move through 2026 and beyond, the Internal Audit Review initiative will expand with a deliberate focus on education, advocacy, and connection.

  • Education, by supporting continuous professional learning through articles, resources, and future learning initiatives that bridge theory and practice.

  • Advocacy, by contributing to informed discussion about the value, independence, and positioning of internal audit within organisations and across society.

  • Connection, by fostering a professional community where auditors can share perspectives, learn from one another, and engage across sectors and geographies.

The intention is not to speak at the profession, but to grow with it — shaped by the challenges practitioners face and the insights they bring.

An Independent and Practitioner-Focused Voice

A defining principle of Internal Audit Review is independence — not only in the assurance sense, but in thought. The Review is not aligned to any single methodology, sector, or commercial interest. Its credibility will rest on the quality of ideas it presents and the integrity of the discussions it hosts.

Contributors are encouraged to challenge assumptions, explore grey areas, and reflect honestly on what works, what does not, and what remains unresolved. Internal audit does not exist in a vacuum, and neither should the conversations that shape it.

By creating space for thoughtful, sometimes uncomfortable, but always constructive dialogue, Internal Audit Review aims to contribute meaningfully to the profession’s long-term development.

An Invitation to Engage

This first edition is both a beginning and a call to action.

Whether you are an experienced chief audit executive, a developing practitioner, a risk or governance professional, or someone with an interest in assurance and organisational resilience, you are invited to engage with Internal Audit Review. Read critically. Reflect openly. Contribute generously.

Future editions will be strengthened by diverse voices and perspectives — from different industries, regions, and career stages. The success of this initiative will not be measured solely by readership, but by the quality of conversation it enables and the professional confidence it helps to build.

Looking Ahead

Internal audit has always been a profession grounded in judgement, ethics, and public trust. As its role continues to evolve, so too must the ways in which we learn, share, and lead.

Internal Audit Review is committed to being part of that evolution — not as a definitive authority, but as a trusted forum for insight, discussion, and connection.

Thank you for being part of this first edition. I look forward to the dialogue ahead.

Thomas Bullman
Founder and Executive Director
Internal Audit Review

Editor’s Introduction Edition 1, Q1, 2026

The Internal Audit Review has been created at a time when the internal audit profession is undergoing profound change. Expectations placed on internal auditors have expanded rapidly, while the risk landscape has grown more complex, interconnected, and less predictable. Traditional models of assurance, while still essential, are no longer sufficient on their own. Internal audit is increasingly expected to provide insight, foresight, and perspective — to help organisations navigate uncertainty rather than merely confirm compliance.

This publication exists to support that evolution.

Why Internal Audit Review, and Why Now

Across industries and sectors, internal audit functions are being asked to do more with less, to cover broader risk universes, and to operate at greater strategic altitude. Digital transformation, cyber risk, regulatory change, data integrity, environmental and social responsibility, and geopolitical volatility have all reshaped the assurance agenda. At the same time, audit committees and executive management expect internal audit to be relevant, timely, and forward-looking.

Yet many practitioners experience this shift in isolation — grappling with new expectations without always having access to practical insight, peer learning, or space for thoughtful reflection. Internal Audit Review was conceived in response to that gap.

This journal is intended to be a place where ideas can be explored with depth, where emerging challenges can be examined critically, and where professional judgement is valued as much as technical compliance. It is not designed to replicate standards or restate guidance already available elsewhere. Instead, it seeks to complement them by focusing on interpretation, application, and lived experience within the profession.

A Quarterly Space for Reflection and Insight

As a quarterly publication, Internal Audit Review is deliberately paced. In a world saturated with rapid commentary and fleeting opinion, there remains a need for considered analysis — writing that allows time to reflect, connect themes, and extract meaning.

Each edition will focus on issues shaping the present and future of internal audit, drawing on contributions from practitioners, leaders, and subject-matter experts. Articles will range from strategic perspectives and thematic analysis to practical insights grounded in real-world audit environments. Over time, the Review aims to build a body of work that reflects the maturity, diversity, and evolving nature of the profession.

This first edition sets the tone. It marks the beginning of an ongoing conversation — one that will develop, deepen, and broaden with each subsequent issue.

Beyond a Journal: An Evolving Initiative

While Internal Audit Review takes shape initially as a publication, it represents only one part of a broader initiative.

As we move through 2026 and beyond, the Internal Audit Review initiative will expand with a deliberate focus on education, advocacy, and connection.

  • Education, by supporting continuous professional learning through articles, resources, and future learning initiatives that bridge theory and practice.

  • Advocacy, by contributing to informed discussion about the value, independence, and positioning of internal audit within organisations and across society.

  • Connection, by fostering a professional community where auditors can share perspectives, learn from one another, and engage across sectors and geographies.

The intention is not to speak at the profession, but to grow with it — shaped by the challenges practitioners face and the insights they bring.

An Independent and Practitioner-Focused Voice

A defining principle of Internal Audit Review is independence — not only in the assurance sense, but in thought. The Review is not aligned to any single methodology, sector, or commercial interest. Its credibility will rest on the quality of ideas it presents and the integrity of the discussions it hosts.

Contributors are encouraged to challenge assumptions, explore grey areas, and reflect honestly on what works, what does not, and what remains unresolved. Internal audit does not exist in a vacuum, and neither should the conversations that shape it.

By creating space for thoughtful, sometimes uncomfortable, but always constructive dialogue, Internal Audit Review aims to contribute meaningfully to the profession’s long-term development.

An Invitation to Engage

This first edition is both a beginning and a call to action.

Whether you are an experienced chief audit executive, a developing practitioner, a risk or governance professional, or someone with an interest in assurance and organisational resilience, you are invited to engage with Internal Audit Review. Read critically. Reflect openly. Contribute generously.

Future editions will be strengthened by diverse voices and perspectives — from different industries, regions, and career stages. The success of this initiative will not be measured solely by readership, but by the quality of conversation it enables and the professional confidence it helps to build.

Looking Ahead

Internal audit has always been a profession grounded in judgement, ethics, and public trust. As its role continues to evolve, so too must the ways in which we learn, share, and lead.

Internal Audit Review is committed to being part of that evolution — not as a definitive authority, but as a trusted forum for insight, discussion, and connection.

Thank you for being part of this first edition. I look forward to the dialogue ahead.

Thomas Bullman
Founder and Executive Director
Internal Audit Review

New Certified Internal Audit Exam - What has changed?

This article can be interesting for CIAs who earned thier titles based on former syllabi so as to be able to give information current candidates in case of any questions arises.


Why the CIA Exam Changed in 2025?

The Institute of Internal Auditors (IIA) revamped the CIA exam to:

·       Reflect modern global internal audit practices

·       Align with the new Global Internal Audit Standards™ (effective January 9, 2025)

·       Reduce redundancy across the three exam parts

·       Clarify required knowledge and skills for candidates

The 2025 CIA exam update isn’t just a refresh based on new Global Audit Standards —it’s a redefinition.


Transition Rules You Should Know

Rule 1: English exam transitions was on May 28, 2025. Other languages follow a staggered schedule (e.g., Japanese: July 28, Spanish: October 28, etc.)

Rule 2: Passed parts under the old syllabus remain valid for 3 years from registration. If you passed any part before the new syllabus launches, you don’t need to retake it under the new format—unless your program expires. You can mix new parts with formerly passed parts (e.g., Part 1 & 2 before May 28, 2025 + Part 3 after) and still earn the CIA designation. Several other combinations are possible if we consider the languages as well. But to buy new learning materials is highly recommended

No Prescribed Order: You can take the parts in any order. The IIA doesn’t require a specific sequence.


Part 1: Internal Audit Fundamentals

The name of Exam Part 1 was changed from "Essentials of Internal Auditing" to "Internal Audit Fundamentals". There were moderate adjustments, mainly refining foundational concepts. It can be said that Part 1 has changed the least in the entire CIA exam system; in other words, it has the most in common with the old syllabus.


Foundations

Syllabus 2019

Syllabus 2025

I. Foundations of Internal Auditing 15 %

A) Foundations of Internal Auditing 35 %


The weight of this unit has apparently increased significantly; however, this does not necessarily mean that the fundamentals will be scrutinized much more deeply. Rather, it is a consolidation of core Standard components based on the new system. Crucially, quite a few subunits from the former Part 2 (such as the types of Assurance and Advisory Services) have been moved here. This consolidation is visually indicated by the blue-to-gray color transition in the pie chart, showing that the section is composed of more than just its original content.


Independence, ethics, professionalism

Syllabus 2019

Syllabus 2025

II. Independence & Objectivity 15 %

B) Ethics & Professionalism 20 %

III. Proficiency & Due Professional Care 18%


Two main units, which were closely related anyway, were merged into one. Overall, their combined weight decreased from 33% to 20%. This reduction does not mean that the importance of these units has diminished. These principles will also arise later in several other practical issues in Part 2 and Part 3.


QAIP moved to Part 3

Syllabus 2019

Syllabus 2025

IV. Quality Assurance and Improvement Program

Moved to Part 3


This unit was entirely moved to Part 3 (Internal Audit Function). This strategic shift highlights that the overall management and governance of the internal audit function, including QAIP, is now considered a strategic, leadership-level responsibility, appropriate for the redefined Part 3 syllabus.


GRC and Fraud Risk

Syllabus 2019

Syllabus 2025

V. Governance, Risk Management, Control 35 %

C) Governance, Risk Management, Control 30 %

VI. Fraud Risk 10 %

D) Fraud Risk 15 %


There were no significant thematic changes to GRC, just a minor adjustment in weight. Conversely, the weight assigned to Fraud Risk increased, indicating a growing emphasis on understanding the basic principles of fraud schemes and detection even at the foundational level of the CIA exam.


Part 2: Internal Audit Engagement

The name of Exam Part 2 was changed from „Practice of Internal Auditing” to „Internal Audit Engagement”. While this content underwent significant restructuring—arguably more so than Part 1, but certainly less than Part 3 — this overhaul is visually summarized in the figure below.



Managing the IAA moved to Part 1 & 3.

Syllabus 2019

Syllabus 2025

I. Managing the Internal Audit Activity 20 %

Moved to Part 1

Moved to Part 3 and split into two units


This unit was eliminated from Part 2, but Its content was redistributed across the other two exam parts:

·       New Part 1: As we discussed in the previous article concerning Part 1 changes, several subunits from the former Part 2 (such as the types of Assurance and Advisory Services) have been relocated to Part 1.

·       New Part 3: Other subunits from this eliminated unit were moved to Part 3, which itself was subsequently split into two distinct Units. We will provide a detailed analysis of these specific changes in the article dedicated to Part 3 revisions.


Planning

Syllabus 2019

Syllabus 2025

II. Planning the Engagement 20 %

A) Engagment Planning 50 %


Frequently, the professional community debated whether the elimination of the former Part 3, "Business Knowledge for Internal Auditing," which was widely considered the most difficult of the three parts, would lead to an excessively simplified path to achieving the Certified Internal Auditor (CIA) designation. A closer examination of the curriculum, moving beyond the mere unit and subunit titles, reveals that this assumption is inaccurate. In addition, the unit's weighting dramatically increased from 20% to a substantial 50%.

While the former content of Engagement Planning has largely remained consistent, the dramatic increase in weighting is due to the integration of several critical subunits (Basic Acumen as well as IT related units) migrated from the former Part 3 (Business Knowledge for Internal Auditing).

What is the fundamental conclusion we can draw from this restructuring? The former Part 3 material was arguably somewhat disjointed or isolated within the curriculum. Now, some of the previous Part 3 units have been directly linked to the practical components of the exam. While the weighting of these specific units may appear reduced as they are now embedded within other exam parts, the overall workload and complexity for the candidates have certainly not decreased.

Performing

Syllabus 2019

Syllabus 2025

III. Performing the Engagement 40 %

B) Information Gathering, Analysis and Evaluation 40 %


The weight of this section remained constant at 40%. Although the subunit has been renamed, its content has not changed significantly. Consequently, most study materials are expected to feature very similar chapters on this unit.


Communication and Monitoring – stayed and/or moved to Part 3

Syllabus 2019

Syllabus 2025

IV. Communicating Engagement and Monitoring Progress 20 %

C) Engagement Supervision & Communication 10 %

Moved to Part 3


The content of the former Part 2 subunit was effectively split into two components:

·       New Part 2: Some core elements were retained in the new Part 2 syllabus under unit C.

·       New Part 3: Most of the key elements, particularly those related to Recommendations and Monitoring, have been relocated to the new Part 3 exam Unit D.

This split unit now accounts for 10% of the new Part 2 exam, while the relocated elements constitute 45% of the new Part 3 exam. This combined weighting significantly surpasses the former Part 2's weighting for these activities (which was 20 %), ultimately indicating an overall increased importance placed on the Value Added Activity of Internal Auditing.


Part 3: Functions of Internal Auditing

The new Part 3 is almost entirely different from its predecessor. The former Part 3 was entitled "Business Knowledge for Internal Auditing". The new Part 3 fundamentally represents a consolidation of the former Part 1 and Part 2 Units that focused specifically on the management activities of the Chief Audit Executive (CAE). By aggregating these key leadership responsibilities into a dedicated exam part, the unit of Internal Audit Management now receives significantly greater attention and weighting compared to previous syllabi.

The summary of these massive changes is illustrated in the figure below, which clearly reflects the scale of this restructuring.



The Fate of Former Part 3 Units

Syllabus 2019

Syllabus 2025

I. Business Acumen 35 %

Partially moved to Part 2

Small subunits stayed in Part 3

Partially eliminated

II. Information security 25 %

III. Information Technology 20 %

IV.  Financial Management 20 %


Its four main units have not been totally eliminated from the CIA exams, but have been strategically relocated:

·       Several units and subunits moved to the new Part 2 A) Engagement Planning, as discussed.

·       Some smaller subunits retained in the new Part 3 A) Internal Audit Operations (specifically within Resource management).

·       Certain highly specialized areas (Managerial accounting concepts, Costing systems, and specific IT infrastructure concepts) are no longer directly included in the exam syllabus.


Relocation of IAA Management from Part 2

Syllabus 2019

Syllabus 2025

From Part 2 I. Managing the IAA 

A) Internal Audit Operations 25%

B) Internal Audit Plan 15 %


2019 Part 2 I. Managing the Internal Audit Activity VS 2025 Part 3 A) Internal Audit Operation & B) Internal Audit Plan

The former Part 2 Unit was not only moved to the New Part 3 (and Part 1), but was also split into two distinct Units. Collectively, the relocated elements received a greater weighting: increasing from 20% up to 40%. Furthermore, considering some subunits moved to Part 1, the total focus increase on these management units is substantial.


QAIP from Part 1

Syllabus 2019

Syllabus 2025

From Part I. QAIP

C) QAIP 15 %


The weighting of Quality Assurance and Improvement Program (QAIP) has also increased. It was 7% in the former Part 1, but is now weighted at 15% in the new Part 3 syllabus.


Emphasis on Value-Added Outcomes from Part 2

Syllabus 2019

Syllabus 2025

Form Part 2

D) Engagement Results & Monitoring 45 %


As noted, roughly half of this former Part 2 unit was retained in the new Part 2 (at 10%), while the other half was relocated to Part 3. The total weighting of this split section significantly increased from 20% up to 55% (10% in Part 2 and 45% in Part 3). This huge increase indicates that internal auditing must deliver tangible results to add Value to the Organization.

The changes and transitions can be summarized in the following charts:

Conclusion

The overall structure clearly indicates that the CIA program has been strategically reorganized to follow the real-life audit flow: Fundamentals (Part 1) lead to Planning and Execution (Part 2), which culminates in Management and Value-Added Outcomes (Part 3). The result is a more integrated and practically relevant certification designed to prepare auditors for leadership roles.

Is the new system easier or harder for candidates? Which is the hardest Part, and which is the easiest part of the new system. There is no universal answer this question. It depends on each candidates interest and experience.


Sources:

2019 CIA Syllabi

2025 CIA Syllabi

https://www.theiia.org/en/certifications/cia/exam-prep-resources/exam-syllabus/

Gleim CIA products

https://www.gleim.com/cia-review/

Zain Academy CIA products

https://zainacademy.us/product-category/cia-exam-review/


Charts are created by EXCEL and Paint




Jan 12, 2026

9 min read

Transforming Internal Audit: The Role of Artificial Intelligence

These processes include learning (the acquisition of information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions), and self-correction. AI aims to create systems that can perform tasks that would normally require human intelligence, such as visual perception, speech recognition, decision-making, and language translation.


Major Components of Artificial Intelligence


AI is comprised of several key components, each contributing to the development and functioning of intelligent systems. The major components include:


1. Machine Learning (ML)


Machine Learning is a subset of AI that involves the use of algorithms and statistical models to enable systems to improve their performance on a specific task through experience. Rather than being explicitly programmed to perform a task, ML algorithms use data to learn and make decisions.


  • Supervised Learning: Algorithms are trained on labeled data, meaning the input comes with the correct output. The model learns to map inputs to outputs and is evaluated based on its performance on a validation dataset.

  • Unsupervised Learning: Algorithms are used to identify patterns in data without labeled responses. This is useful for clustering data into groups based on similarities.

  • Reinforcement Learning: Algorithms learn to make decisions by receiving rewards or penalties for actions taken, aiming to maximize cumulative reward.


2. Neural Networks


Newral Networks are a series of algorithms that attempt to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. They are the foundation of deep learning models.


  • Artificial Neural Networks (ANNs): Composed of layers of nodes, or neurons, where each node represents a mathematical function. Data passes through these nodes, enabling the network to learn from data.

  • Convolutional Neural Networks (CNNs): Primarily used in image recognition and processing, CNNs apply convolutional layers to preserve the spatial relationships between pixels.

  • Recurrent Neural Networks (RNNs): Used for sequential data, such as time series or natural language, RNNs have connections that loop back on themselves to maintain a memory of previous inputs.


3. Natural Language Processing


Natural Language Processing (NLP) enables machines to understand, interpret, and generate human language. NLP combines computational linguistics with machine learning to process text and speech data.


  • Text Analysis: Involves parsing and understanding text data, such as sentiment analysis, topic modeling, and named entity recognition.

  • Speech Recognition: Converts spoken language into text.

  • Language Generation: Produces human-like text based on input data, often used in chatbots and virtual assistants.


4. Computer Vision


Computer Vision is a field of AI that enables machines to interpret and make decisions based on visual data from the world.


  • Image Classification: Assigning a label to an entire image based on its contents.

  • Object Detection: Identifying and locating objects within an image.

  • Image Segmentation: Partitioning an image into segments to simplify or change the representation of an image into something more meaningful.


5. Robotics


Robotics integrates AI with mechanical engineering to create machines capable of performing tasks autonomously.


  • Sensing: Robots use sensors to gather information about their environment.

  • Planning: Algorithms determine the best course of action based on the robot's goals.

  • Control: Ensuring the robot can execute the planned actions effectively.


Artificial Intelligence in Internal Audit


AI is fundamentally transforming various industries, and Internal Audit is no exception. The integration of AI into audit processes promises to revolutionize the field by automating repetitive tasks, enhancing data analysis capabilities, and improving the accuracy of audit findings. This article explores how AI is revolutionizing Internal Audit, the role of generative AI tools, and addresses critical questions about the future of auditors in an AI-driven world.


Automation of Routine Audit Processes


1. Automating Data Collection and Sampling:


AI significantly reduces the time auditors spend on repetitive tasks such as data collection and sampling. Traditionally, auditors manually gather data from various sources, a process that is not only time-consuming but also prone to human error. AI systems can automate these tasks, efficiently extracting data from multiple sources, including structured databases and unstructured documents.


  • Efficiency: AI tools can process vast amounts of data in seconds, which would take humans days or even weeks.

  • Accuracy: By minimizing human intervention, AI reduces the risk of errors in data collection and sampling, ensuring more accurate audit results.

  • Consistency: AI ensures that data is collected consistently across all audits, improving the reliability of the audit process.


2. AI-Driven Data Analytics for Identifying Anomalies and Patterns:


AI excels in data analysis, particularly in identifying anomalies and patterns that might indicate risks or irregularities. Machine learning algorithms can analyze historical data to establish norms and detect deviations that warrant further investigation.


  • Anomaly Detection: AI algorithms can identify unusual transactions or patterns that could signify fraudulent activities or errors. This capability is crucial for early detection and prevention.

  • Predictive Analytics: AI can predict potential risks by analyzing trends and historical data, allowing auditors to focus on areas with the highest risk.

  • Comprehensive Analysis: AI can handle complex datasets and perform multifaceted analyses, providing deeper insights that traditional methods might miss.


3. Enhancements in Fraud Detection and Risk Assessment:


AI enhances fraud detection and risk assessment by using advanced techniques such as natural language processing (NLP) and machine learning.


  • Real-Time Monitoring: AI systems can continuously monitor transactions and activities, providing real-time alerts for suspicious activities.

  • Risk Scoring: AI can assign risk scores to transactions or entities based on predefined criteria, helping auditors prioritize their efforts.

  • Sentiment Analysis: NLP can analyze communication patterns and sentiments in emails and other documents to detect potential red flags.


Will Artificial Intelligence Replace the Auditor?


While AI offers numerous benefits, it raises the question: Will AI replace the auditor? The consensus among experts is that AI will not replace auditors but rather augment their capabilities.


  • Augmentation Over Replacement: AI handles repetitive and data-intensive tasks, allowing auditors to focus on strategic and judgment-based aspects of the audit. Auditors' expertise in interpreting results, understanding business contexts, and making decisions cannot be fully replicated by AI.

  • New Skill Sets: Auditors will need to develop new skills to work effectively with AI, such as understanding AI outputs, managing AI tools, and interpreting complex data analyses.


The Challenge of Artificial Intelligence "Hallucinations"


AI systems, particularly generative models, can sometimes "hallucinate" and present false information as though it is true. This issue poses a challenge for trust and reliability in AI-driven audits.


  • Understanding Hallucinations: Generative AI models, like ChatGPT, may generate plausible but incorrect information due to biases in training data or inherent limitations in the models.

  • Mitigation Strategies: To mitigate this risk, auditors should cross-verify AI-generated insights with multiple sources and maintain a critical oversight role.


The Role of Generative Artificial Intelligence Tools


Generative AI tools such as ChatGPT, Copilot, and Gemini have the potential to revolutionize the audit landscape, particularly in data analytics.


1. Advantages of Generative AI Tools:


  • Enhanced Data Interpretation: Generative AI can help interpret complex data sets and generate insightful summaries.

  • Automated Reporting: These tools can automate the creation of audit reports, saving time and improving consistency.

  • Interactive Analysis: Generative AI can assist auditors by answering queries in real-time, providing a more interactive and dynamic analysis process.


2. Potential Disadvantages of Generative AI Tools:


  • Accuracy Concerns: The risk of AI-generated misinformation or hallucinations requires careful oversight and validation.

  • Bias and Fairness: AI models can inherit biases from training data, leading to biased outcomes if not properly managed.

  • Dependence on Technology: Over-reliance on AI tools may lead to a decline in auditors’ critical thinking and analytical skills.


Evaluating Artificial Intelligence's Role in the Audit Workflow


Areas Where Generative AI Can Benefit the Audit Workflow:


  • Data Analysis: Enhancing the ability to analyze large datasets quickly and accurately.

  • Report Generation: Streamlining the process of creating detailed and consistent audit reports.

  • Continuous Monitoring: Enabling real-time monitoring and alerting for potential issues.


Areas Where AI Should Be Avoided:


  • Final Judgment: AI should not replace human judgment in making final audit decisions.

  • Ethical Evaluations: Complex ethical considerations and decisions should remain within the purview of human auditors.


Challenges in Integrating Artificial Intelligence in Internal Audit Processes


The integration of AI into Internal Audit processes presents numerous opportunities for efficiency and accuracy but also brings several challenges. These challenges can be broadly categorized into technical, organizational, ethical, and regulatory aspects. Here are some of the key challenges:


1. Technical Challenges


  • Data Quality and Availability: AI systems rely heavily on high-quality, structured data to function effectively. In many organizations, data is often siloed, inconsistent, or incomplete, making it difficult to leverage AI fully.

  • Integration with Existing Systems: Integrating AI tools with existing audit and enterprise systems can be complex and costly. Legacy systems may not be compatible with modern AI technologies, requiring significant upgrades or replacements.

  • Algorithm Transparency and Explainability: AI models, especially those based on deep learning, can be "black boxes," making it difficult for auditors to understand how decisions are made. This lack of transparency can be a significant barrier to trust and acceptance.


2. Organizational Challenges


  • Change Management: Integrating AI into audit processes requires a cultural shift and buy-in from all levels of the organization. Resistance to change from employees accustomed to traditional methods can hinder AI adoption.

  • Skills and Expertise: There is a need for new skills and expertise to manage and work with AI tools. Training auditors to understand and use AI effectively is essential but can be resource-intensive.


3. Ethical and Regulatory Challenges


  • Bias and Fairness: AI systems can inherit biases from the data they are trained on, leading to unfair or discriminatory outcomes. Ensuring that AI operates fairly and ethically is a significant concern.

  • Data Privacy and Security: AI systems often require access to large datasets, which can include sensitive or personal information. Ensuring data privacy and security while using AI is critical and challenging.

  • Regulatory Compliance: As AI technologies evolve, regulatory frameworks may lag, creating uncertainty about compliance requirements. Auditors need to stay informed about changing regulations and ensure that AI applications comply with all relevant laws.


Examples of Success When Integrating Artificial Intelligence in Internal Audit Processes


Integrating AI into Internal Audit processes can lead to significant improvements in efficiency, accuracy, and risk management. Here are three examples of organizations that have successfully implemented AI in their Internal Audit functions:


Example 1: JPMorgan Chase Enhances Fraud Detection


Situation: JPMorgan Chase, one of the largest financial institutions in the world, faced challenges in detecting and preventing fraudulent transactions due to the sheer volume of transactions processed daily.

Actions Taken:


  • Implementation of AI-Powered Analytics: JPMorgan Chase implemented AI-driven analytics tools to monitor transactions in real-time. Machine learning algorithms were trained on historical transaction data to identify patterns and anomalies indicative of fraud.

  • Automated Alerts: The system was configured to generate automated alerts for transactions that deviated from established norms, enabling rapid response and investigation.


Outcome:


  • Increased Detection Rate: The financial institution saw a significant increase in the detection rate of fraudulent transactions. AI identified complex fraud schemes that traditional methods missed.

  • Reduced False Positives: The precision of AI algorithms reduced the number of false positives, streamlining the investigation process and improving efficiency.

  • Enhanced Compliance: JPMorgan Chase enhanced its compliance with regulatory requirements by demonstrating robust fraud detection and prevention mechanisms.


Example 2: General Electric Optimizes Risk Management


Situation: General Electric, a global manufacturing conglomerate, struggled with effectively assessing and managing operational risks across its extensive supply chain.


Actions Taken:


  • AI-Based Risk Assessment: GE deployed AI tools to analyze data from various sources, including supply chain logistics, production data, and market trends. Machine learning models were used to predict potential risks and disruptions.

  • Predictive Maintenance: AI was utilized to implement predictive maintenance for critical machinery, using sensors and historical data to forecast equipment failures and schedule timely maintenance.


Outcome:


  • Improved Risk Mitigation: The AI-driven risk assessment provided early warnings of potential disruptions, allowing GE to mitigate risks proactively.

  • Cost Savings: Predictive maintenance reduced unplanned downtime and maintenance costs, leading to significant operational savings.

  • Operational Efficiency: The integration of AI optimized supply chain management, improving overall operational efficiency and resilience.


Example 3: Walmart Enhances Audit Accuracy and Efficiency


Situation: Walmart, the world's largest retailer, faced difficulties in conducting timely and accurate internal audits across its numerous stores due to the large volume of transactions and data.


Actions Taken:


  • AI-Driven Audit Automation: Walmart implemented AI tools to automate the data collection and analysis process for internal audits. Natural language processing (NLP) was used to analyze and extract relevant information from unstructured data such as emails and documents.

  • Anomaly Detection: Machine learning algorithms were employed to identify anomalies and irregularities in financial transactions and inventory records.


Outcome:


  • Increased Audit Efficiency: The automation of routine audit tasks significantly reduced the time required to complete audits, allowing the Internal Audit team to focus on high-value activities.

  • Enhanced Accuracy: AI-driven anomaly detection improved the accuracy of audits by identifying discrepancies that manual processes overlooked.

  • Actionable Insights: Walmart gained actionable insights into operational inefficiencies and areas for improvement, leading to better decision-making and strategic planning.



Conclusion


Artificial Intelligence is transforming the field of Internal Audit by automating routine tasks, enhancing data analysis, and improving the accuracy of audit findings. While AI will not replace auditors, it will enhance their capabilities, allowing them to focus on more strategic and judgment-based tasks. Generative AI tools like ChatGPT, Copilot, and Gemini offer significant benefits but also pose challenges that require careful management. By leveraging AI effectively and addressing its limitations, Internal Auditors can significantly enhance their impact and contribute to more robust and reliable audit processes.

Jan 12, 2026

11 min read

When, “Let’s Soften This” becomes an Auditor’s Risk

The conversation is almost always polite and rational: “The issue is being addressed.” “This could trigger unnecessary regulatory questions.”Let us deal with it operationally first.”

I have been in this conversation more times than I can count and in the moment it always sounds reasonable and common sense. That is what makes it dangerous.

This is not an abstract ethics debate, but a recurring challenge, particularly in highly regulated environments like banking, insurance or aviation. It carries a risk that is rarely named. I think of it as Deferred Accountability Risk.

Why this pressure arises and why auditors hesitate

In my experience, management pressure to soften audit reporting usually comes from understandable places.

There is often genuine regulatory anxiety. Senior executives know that audit reports can be accessed by regulators and read without context. Even balanced findings can look stark when lifted out of a broader discussion, so the instinct to manage the written record is not irrational.

There is also reputational concern. Once something is formally escalated to the Audit Committee, it becomes part of the governance record and cannot be unseen or quietly resolved. Softening language is often presented as buying time to fix the issue before it attracts attention or escalates beyond proportion.

There is also the reality of working relationships. Internal audit operates inside the organisation. Escalation can be portrayed as being rigid or disconnected from operational realities, even when the concern itself is valid.

None of this makes management unreasonable. In fact, this is precisely why auditors pause. The request appeals to pragmatism rather than compromise. The problem is that what feels like pragmatism at the time can quietly change where accountability sits.

What Deferred Accountability really looks like in practice

Deferred accountability risk arises when an organisation chooses to delay formal recognition of an issue in order to avoid immediate discomfort or scrutiny. Accountability does not vanish. It waits.

What often goes unspoken in these moments is what management is functionally asking internal audit to do. When management asks audit to soften or omit an issue from a report, they are not just asking for a different turn of phrase. They are asking audit to step slightly outside its assurance role and carry part of management’s accountability for how and when the issue is formally acknowledged.

That shift matters and at that point, the audit report stops being a mirror and starts becoming a shield for management.

While the exposure may not be obvious immediately, it becomes very obvious later, when questions are asked about who knew what, and when.

What history keeps showing us

Large corporate failures are often described as sudden but they rarely are. More often, they follow a slow, familiar pattern where issues are raised, explanations are accepted, discomfort is managed and escalation is delayed.

The sales practices scandal at Wells Fargo is a case I keep returning to. What struck me most when revisiting the timeline was not the scale of the misconduct, but how early the warning signs appeared. Internal concerns existed for years. What failed was not awareness, but escalation with enough clarity and persistence to force governance attention. When accountability finally arrived, it came through regulators, and by then the damage was extensive.

This pattern is not confined to one jurisdiction.

In India, the collapse of Satyam Computer Services showed how comfort can gradually replace verification. Cash balances were accepted rather than aggressively challenged. Each reporting cycle deferred the reckoning. When accountability arrived, it arrived publicly and painfully, with consequences that extended beyond the company itself.

In South Africa, Steinhoff International followed a similar trajectory. Complex structures produced plausible explanations. Concerns were absorbed rather than escalated. Oversight mechanisms existed, but they did not interrupt the pattern. The eventual collapse wiped out billions in value and directly affected pension funds and ordinary investors.

In none of these cases was accountability avoided. It was postponed, and the cost of that postponement compounded over time.

In these cases, early warning signs and governance red flags were present for years before decisive action occurred — whether through internal controls, risk reporting, or escalation to governance bodies — underscoring how accountability was continually deferred until regulators intervened or crises erupted.

Why agreeing to soften can feel like the sensible option

From the auditor’s point of view, agreeing to soften language can feel like a reasonable compromise. The issue is known. Management appears engaged. The relationship is preserved. The auditor retains influence.

The difficulty is that audit reports are not just internal communications. They are governance artefacts. What is included, excluded, or diluted shapes what the Audit Committee knows and when it knows it.

Once something is left out of the formal record, bringing it back later is rarely straightforward. Auditors often underestimate how irreversible these decisions are.

From an Audit Committee perspective, the question after a failure is not whether management felt pressured or whether wording was polite. It is whether the committee had enough information, early enough, to act.

That is where deferred accountability becomes personal.

How I have seen experienced auditors think it through

The most effective auditors I have worked with do not treat this as a binary choice between compliance and confrontation. They ask themselves uncomfortable questions.

·       How would this decision read if examined in hindsight? Not by colleagues, but by a regulator or inquiry that does not know the personalities involved.

·       Am I, by softening this, making a judgement on timing that properly belongs to the Audit Committee?

·       Am I assuming this can be revisited later, when in reality that may be much harder than it sounds?

·       Is this an isolated accommodation, or one more small adjustment in what will become a pattern?

These are not checklist questions. They are judgement calls, but they change the nature of the conversation.

Navigating the issue without burning bridges

Independence does not require theatrics. It requires clarity.

In practice, experienced Chief Audit Executives often separate recognition from remediation. They ensure that the issue and its implications are clearly articulated for the Audit Committee, while still acknowledging management’s response and context.

They stay anchored to a simple reality. While day-to-day interactions may be with management, internal audit ultimately exists to support the Audit Committee’s oversight responsibilities. When in doubt, they ensure the committee has enough information to make its own call.

That stance does not always make life easier. It does tend to build long-term credibility.

The cost of getting this wrong

The cost of deferred accountability is rarely borne by those who ask for softening in the moment. It is borne later, often by people who had no part in the original conversation.

Post-incident reviews do not dwell on tone. They focus on whether warning signs were surfaced, whether escalation was timely, and whether governance bodies were adequately informed.

I have yet to see an inquiry conclude that an organisation failed because an audit report was too clear.

A final reflection

Internal audit adds the most value when it surfaces accountability while it is still manageable.

When reporting is diluted to preserve short-term harmony, organisations do not eliminate risk. They merely decide when and how accountability will arrive, often without realising they have made that choice.

Deferred Accountability Risk is not about idealism or confrontation. It is about stewardship. Some of the most consequential audit judgements are not about what we find, but how clearly and when we choose to say it.

Navin Pasricha, a former CAE, CRO and Audit Committee Member, is author of,Getting Ready to Roar: Chief Auditor’s Guide from Audit Room to Board Room.”


Jan 12, 2026

6 min read

New Certified Internal Audit Exam - What has changed?

This article can be interesting for CIAs who earned thier titles based on former syllabi so as to be able to give information current candidates in case of any questions arises.


Why the CIA Exam Changed in 2025?

The Institute of Internal Auditors (IIA) revamped the CIA exam to:

·       Reflect modern global internal audit practices

·       Align with the new Global Internal Audit Standards™ (effective January 9, 2025)

·       Reduce redundancy across the three exam parts

·       Clarify required knowledge and skills for candidates

The 2025 CIA exam update isn’t just a refresh based on new Global Audit Standards —it’s a redefinition.


Transition Rules You Should Know

Rule 1: English exam transitions was on May 28, 2025. Other languages follow a staggered schedule (e.g., Japanese: July 28, Spanish: October 28, etc.)

Rule 2: Passed parts under the old syllabus remain valid for 3 years from registration. If you passed any part before the new syllabus launches, you don’t need to retake it under the new format—unless your program expires. You can mix new parts with formerly passed parts (e.g., Part 1 & 2 before May 28, 2025 + Part 3 after) and still earn the CIA designation. Several other combinations are possible if we consider the languages as well. But to buy new learning materials is highly recommended

No Prescribed Order: You can take the parts in any order. The IIA doesn’t require a specific sequence.


Part 1: Internal Audit Fundamentals

The name of Exam Part 1 was changed from "Essentials of Internal Auditing" to "Internal Audit Fundamentals". There were moderate adjustments, mainly refining foundational concepts. It can be said that Part 1 has changed the least in the entire CIA exam system; in other words, it has the most in common with the old syllabus.


Foundations

Syllabus 2019

Syllabus 2025

I. Foundations of Internal Auditing 15 %

A) Foundations of Internal Auditing 35 %


The weight of this unit has apparently increased significantly; however, this does not necessarily mean that the fundamentals will be scrutinized much more deeply. Rather, it is a consolidation of core Standard components based on the new system. Crucially, quite a few subunits from the former Part 2 (such as the types of Assurance and Advisory Services) have been moved here. This consolidation is visually indicated by the blue-to-gray color transition in the pie chart, showing that the section is composed of more than just its original content.


Independence, ethics, professionalism

Syllabus 2019

Syllabus 2025

II. Independence & Objectivity 15 %

B) Ethics & Professionalism 20 %

III. Proficiency & Due Professional Care 18%


Two main units, which were closely related anyway, were merged into one. Overall, their combined weight decreased from 33% to 20%. This reduction does not mean that the importance of these units has diminished. These principles will also arise later in several other practical issues in Part 2 and Part 3.


QAIP moved to Part 3

Syllabus 2019

Syllabus 2025

IV. Quality Assurance and Improvement Program

Moved to Part 3


This unit was entirely moved to Part 3 (Internal Audit Function). This strategic shift highlights that the overall management and governance of the internal audit function, including QAIP, is now considered a strategic, leadership-level responsibility, appropriate for the redefined Part 3 syllabus.


GRC and Fraud Risk

Syllabus 2019

Syllabus 2025

V. Governance, Risk Management, Control 35 %

C) Governance, Risk Management, Control 30 %

VI. Fraud Risk 10 %

D) Fraud Risk 15 %


There were no significant thematic changes to GRC, just a minor adjustment in weight. Conversely, the weight assigned to Fraud Risk increased, indicating a growing emphasis on understanding the basic principles of fraud schemes and detection even at the foundational level of the CIA exam.


Part 2: Internal Audit Engagement

The name of Exam Part 2 was changed from „Practice of Internal Auditing” to „Internal Audit Engagement”. While this content underwent significant restructuring—arguably more so than Part 1, but certainly less than Part 3 — this overhaul is visually summarized in the figure below.



Managing the IAA moved to Part 1 & 3.

Syllabus 2019

Syllabus 2025

I. Managing the Internal Audit Activity 20 %

Moved to Part 1

Moved to Part 3 and split into two units


This unit was eliminated from Part 2, but Its content was redistributed across the other two exam parts:

·       New Part 1: As we discussed in the previous article concerning Part 1 changes, several subunits from the former Part 2 (such as the types of Assurance and Advisory Services) have been relocated to Part 1.

·       New Part 3: Other subunits from this eliminated unit were moved to Part 3, which itself was subsequently split into two distinct Units. We will provide a detailed analysis of these specific changes in the article dedicated to Part 3 revisions.


Planning

Syllabus 2019

Syllabus 2025

II. Planning the Engagement 20 %

A) Engagment Planning 50 %


Frequently, the professional community debated whether the elimination of the former Part 3, "Business Knowledge for Internal Auditing," which was widely considered the most difficult of the three parts, would lead to an excessively simplified path to achieving the Certified Internal Auditor (CIA) designation. A closer examination of the curriculum, moving beyond the mere unit and subunit titles, reveals that this assumption is inaccurate. In addition, the unit's weighting dramatically increased from 20% to a substantial 50%.

While the former content of Engagement Planning has largely remained consistent, the dramatic increase in weighting is due to the integration of several critical subunits (Basic Acumen as well as IT related units) migrated from the former Part 3 (Business Knowledge for Internal Auditing).

What is the fundamental conclusion we can draw from this restructuring? The former Part 3 material was arguably somewhat disjointed or isolated within the curriculum. Now, some of the previous Part 3 units have been directly linked to the practical components of the exam. While the weighting of these specific units may appear reduced as they are now embedded within other exam parts, the overall workload and complexity for the candidates have certainly not decreased.

Performing

Syllabus 2019

Syllabus 2025

III. Performing the Engagement 40 %

B) Information Gathering, Analysis and Evaluation 40 %


The weight of this section remained constant at 40%. Although the subunit has been renamed, its content has not changed significantly. Consequently, most study materials are expected to feature very similar chapters on this unit.


Communication and Monitoring – stayed and/or moved to Part 3

Syllabus 2019

Syllabus 2025

IV. Communicating Engagement and Monitoring Progress 20 %

C) Engagement Supervision & Communication 10 %

Moved to Part 3


The content of the former Part 2 subunit was effectively split into two components:

·       New Part 2: Some core elements were retained in the new Part 2 syllabus under unit C.

·       New Part 3: Most of the key elements, particularly those related to Recommendations and Monitoring, have been relocated to the new Part 3 exam Unit D.

This split unit now accounts for 10% of the new Part 2 exam, while the relocated elements constitute 45% of the new Part 3 exam. This combined weighting significantly surpasses the former Part 2's weighting for these activities (which was 20 %), ultimately indicating an overall increased importance placed on the Value Added Activity of Internal Auditing.


Part 3: Functions of Internal Auditing

The new Part 3 is almost entirely different from its predecessor. The former Part 3 was entitled "Business Knowledge for Internal Auditing". The new Part 3 fundamentally represents a consolidation of the former Part 1 and Part 2 Units that focused specifically on the management activities of the Chief Audit Executive (CAE). By aggregating these key leadership responsibilities into a dedicated exam part, the unit of Internal Audit Management now receives significantly greater attention and weighting compared to previous syllabi.

The summary of these massive changes is illustrated in the figure below, which clearly reflects the scale of this restructuring.



The Fate of Former Part 3 Units

Syllabus 2019

Syllabus 2025

I. Business Acumen 35 %

Partially moved to Part 2

Small subunits stayed in Part 3

Partially eliminated

II. Information security 25 %

III. Information Technology 20 %

IV.  Financial Management 20 %


Its four main units have not been totally eliminated from the CIA exams, but have been strategically relocated:

·       Several units and subunits moved to the new Part 2 A) Engagement Planning, as discussed.

·       Some smaller subunits retained in the new Part 3 A) Internal Audit Operations (specifically within Resource management).

·       Certain highly specialized areas (Managerial accounting concepts, Costing systems, and specific IT infrastructure concepts) are no longer directly included in the exam syllabus.


Relocation of IAA Management from Part 2

Syllabus 2019

Syllabus 2025

From Part 2 I. Managing the IAA 

A) Internal Audit Operations 25%

B) Internal Audit Plan 15 %


2019 Part 2 I. Managing the Internal Audit Activity VS 2025 Part 3 A) Internal Audit Operation & B) Internal Audit Plan

The former Part 2 Unit was not only moved to the New Part 3 (and Part 1), but was also split into two distinct Units. Collectively, the relocated elements received a greater weighting: increasing from 20% up to 40%. Furthermore, considering some subunits moved to Part 1, the total focus increase on these management units is substantial.


QAIP from Part 1

Syllabus 2019

Syllabus 2025

From Part I. QAIP

C) QAIP 15 %


The weighting of Quality Assurance and Improvement Program (QAIP) has also increased. It was 7% in the former Part 1, but is now weighted at 15% in the new Part 3 syllabus.


Emphasis on Value-Added Outcomes from Part 2

Syllabus 2019

Syllabus 2025

Form Part 2

D) Engagement Results & Monitoring 45 %


As noted, roughly half of this former Part 2 unit was retained in the new Part 2 (at 10%), while the other half was relocated to Part 3. The total weighting of this split section significantly increased from 20% up to 55% (10% in Part 2 and 45% in Part 3). This huge increase indicates that internal auditing must deliver tangible results to add Value to the Organization.

The changes and transitions can be summarized in the following charts:

Conclusion

The overall structure clearly indicates that the CIA program has been strategically reorganized to follow the real-life audit flow: Fundamentals (Part 1) lead to Planning and Execution (Part 2), which culminates in Management and Value-Added Outcomes (Part 3). The result is a more integrated and practically relevant certification designed to prepare auditors for leadership roles.

Is the new system easier or harder for candidates? Which is the hardest Part, and which is the easiest part of the new system. There is no universal answer this question. It depends on each candidates interest and experience.


Sources:

2019 CIA Syllabi

2025 CIA Syllabi

https://www.theiia.org/en/certifications/cia/exam-prep-resources/exam-syllabus/

Gleim CIA products

https://www.gleim.com/cia-review/

Zain Academy CIA products

https://zainacademy.us/product-category/cia-exam-review/


Charts are created by EXCEL and Paint




Transforming Internal Audit: The Role of Artificial Intelligence

These processes include learning (the acquisition of information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions), and self-correction. AI aims to create systems that can perform tasks that would normally require human intelligence, such as visual perception, speech recognition, decision-making, and language translation.


Major Components of Artificial Intelligence


AI is comprised of several key components, each contributing to the development and functioning of intelligent systems. The major components include:


1. Machine Learning (ML)


Machine Learning is a subset of AI that involves the use of algorithms and statistical models to enable systems to improve their performance on a specific task through experience. Rather than being explicitly programmed to perform a task, ML algorithms use data to learn and make decisions.


  • Supervised Learning: Algorithms are trained on labeled data, meaning the input comes with the correct output. The model learns to map inputs to outputs and is evaluated based on its performance on a validation dataset.

  • Unsupervised Learning: Algorithms are used to identify patterns in data without labeled responses. This is useful for clustering data into groups based on similarities.

  • Reinforcement Learning: Algorithms learn to make decisions by receiving rewards or penalties for actions taken, aiming to maximize cumulative reward.


2. Neural Networks


Newral Networks are a series of algorithms that attempt to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. They are the foundation of deep learning models.


  • Artificial Neural Networks (ANNs): Composed of layers of nodes, or neurons, where each node represents a mathematical function. Data passes through these nodes, enabling the network to learn from data.

  • Convolutional Neural Networks (CNNs): Primarily used in image recognition and processing, CNNs apply convolutional layers to preserve the spatial relationships between pixels.

  • Recurrent Neural Networks (RNNs): Used for sequential data, such as time series or natural language, RNNs have connections that loop back on themselves to maintain a memory of previous inputs.


3. Natural Language Processing


Natural Language Processing (NLP) enables machines to understand, interpret, and generate human language. NLP combines computational linguistics with machine learning to process text and speech data.


  • Text Analysis: Involves parsing and understanding text data, such as sentiment analysis, topic modeling, and named entity recognition.

  • Speech Recognition: Converts spoken language into text.

  • Language Generation: Produces human-like text based on input data, often used in chatbots and virtual assistants.


4. Computer Vision


Computer Vision is a field of AI that enables machines to interpret and make decisions based on visual data from the world.


  • Image Classification: Assigning a label to an entire image based on its contents.

  • Object Detection: Identifying and locating objects within an image.

  • Image Segmentation: Partitioning an image into segments to simplify or change the representation of an image into something more meaningful.


5. Robotics


Robotics integrates AI with mechanical engineering to create machines capable of performing tasks autonomously.


  • Sensing: Robots use sensors to gather information about their environment.

  • Planning: Algorithms determine the best course of action based on the robot's goals.

  • Control: Ensuring the robot can execute the planned actions effectively.


Artificial Intelligence in Internal Audit


AI is fundamentally transforming various industries, and Internal Audit is no exception. The integration of AI into audit processes promises to revolutionize the field by automating repetitive tasks, enhancing data analysis capabilities, and improving the accuracy of audit findings. This article explores how AI is revolutionizing Internal Audit, the role of generative AI tools, and addresses critical questions about the future of auditors in an AI-driven world.


Automation of Routine Audit Processes


1. Automating Data Collection and Sampling:


AI significantly reduces the time auditors spend on repetitive tasks such as data collection and sampling. Traditionally, auditors manually gather data from various sources, a process that is not only time-consuming but also prone to human error. AI systems can automate these tasks, efficiently extracting data from multiple sources, including structured databases and unstructured documents.


  • Efficiency: AI tools can process vast amounts of data in seconds, which would take humans days or even weeks.

  • Accuracy: By minimizing human intervention, AI reduces the risk of errors in data collection and sampling, ensuring more accurate audit results.

  • Consistency: AI ensures that data is collected consistently across all audits, improving the reliability of the audit process.


2. AI-Driven Data Analytics for Identifying Anomalies and Patterns:


AI excels in data analysis, particularly in identifying anomalies and patterns that might indicate risks or irregularities. Machine learning algorithms can analyze historical data to establish norms and detect deviations that warrant further investigation.


  • Anomaly Detection: AI algorithms can identify unusual transactions or patterns that could signify fraudulent activities or errors. This capability is crucial for early detection and prevention.

  • Predictive Analytics: AI can predict potential risks by analyzing trends and historical data, allowing auditors to focus on areas with the highest risk.

  • Comprehensive Analysis: AI can handle complex datasets and perform multifaceted analyses, providing deeper insights that traditional methods might miss.


3. Enhancements in Fraud Detection and Risk Assessment:


AI enhances fraud detection and risk assessment by using advanced techniques such as natural language processing (NLP) and machine learning.


  • Real-Time Monitoring: AI systems can continuously monitor transactions and activities, providing real-time alerts for suspicious activities.

  • Risk Scoring: AI can assign risk scores to transactions or entities based on predefined criteria, helping auditors prioritize their efforts.

  • Sentiment Analysis: NLP can analyze communication patterns and sentiments in emails and other documents to detect potential red flags.


Will Artificial Intelligence Replace the Auditor?


While AI offers numerous benefits, it raises the question: Will AI replace the auditor? The consensus among experts is that AI will not replace auditors but rather augment their capabilities.


  • Augmentation Over Replacement: AI handles repetitive and data-intensive tasks, allowing auditors to focus on strategic and judgment-based aspects of the audit. Auditors' expertise in interpreting results, understanding business contexts, and making decisions cannot be fully replicated by AI.

  • New Skill Sets: Auditors will need to develop new skills to work effectively with AI, such as understanding AI outputs, managing AI tools, and interpreting complex data analyses.


The Challenge of Artificial Intelligence "Hallucinations"


AI systems, particularly generative models, can sometimes "hallucinate" and present false information as though it is true. This issue poses a challenge for trust and reliability in AI-driven audits.


  • Understanding Hallucinations: Generative AI models, like ChatGPT, may generate plausible but incorrect information due to biases in training data or inherent limitations in the models.

  • Mitigation Strategies: To mitigate this risk, auditors should cross-verify AI-generated insights with multiple sources and maintain a critical oversight role.


The Role of Generative Artificial Intelligence Tools


Generative AI tools such as ChatGPT, Copilot, and Gemini have the potential to revolutionize the audit landscape, particularly in data analytics.


1. Advantages of Generative AI Tools:


  • Enhanced Data Interpretation: Generative AI can help interpret complex data sets and generate insightful summaries.

  • Automated Reporting: These tools can automate the creation of audit reports, saving time and improving consistency.

  • Interactive Analysis: Generative AI can assist auditors by answering queries in real-time, providing a more interactive and dynamic analysis process.


2. Potential Disadvantages of Generative AI Tools:


  • Accuracy Concerns: The risk of AI-generated misinformation or hallucinations requires careful oversight and validation.

  • Bias and Fairness: AI models can inherit biases from training data, leading to biased outcomes if not properly managed.

  • Dependence on Technology: Over-reliance on AI tools may lead to a decline in auditors’ critical thinking and analytical skills.


Evaluating Artificial Intelligence's Role in the Audit Workflow


Areas Where Generative AI Can Benefit the Audit Workflow:


  • Data Analysis: Enhancing the ability to analyze large datasets quickly and accurately.

  • Report Generation: Streamlining the process of creating detailed and consistent audit reports.

  • Continuous Monitoring: Enabling real-time monitoring and alerting for potential issues.


Areas Where AI Should Be Avoided:


  • Final Judgment: AI should not replace human judgment in making final audit decisions.

  • Ethical Evaluations: Complex ethical considerations and decisions should remain within the purview of human auditors.


Challenges in Integrating Artificial Intelligence in Internal Audit Processes


The integration of AI into Internal Audit processes presents numerous opportunities for efficiency and accuracy but also brings several challenges. These challenges can be broadly categorized into technical, organizational, ethical, and regulatory aspects. Here are some of the key challenges:


1. Technical Challenges


  • Data Quality and Availability: AI systems rely heavily on high-quality, structured data to function effectively. In many organizations, data is often siloed, inconsistent, or incomplete, making it difficult to leverage AI fully.

  • Integration with Existing Systems: Integrating AI tools with existing audit and enterprise systems can be complex and costly. Legacy systems may not be compatible with modern AI technologies, requiring significant upgrades or replacements.

  • Algorithm Transparency and Explainability: AI models, especially those based on deep learning, can be "black boxes," making it difficult for auditors to understand how decisions are made. This lack of transparency can be a significant barrier to trust and acceptance.


2. Organizational Challenges


  • Change Management: Integrating AI into audit processes requires a cultural shift and buy-in from all levels of the organization. Resistance to change from employees accustomed to traditional methods can hinder AI adoption.

  • Skills and Expertise: There is a need for new skills and expertise to manage and work with AI tools. Training auditors to understand and use AI effectively is essential but can be resource-intensive.


3. Ethical and Regulatory Challenges


  • Bias and Fairness: AI systems can inherit biases from the data they are trained on, leading to unfair or discriminatory outcomes. Ensuring that AI operates fairly and ethically is a significant concern.

  • Data Privacy and Security: AI systems often require access to large datasets, which can include sensitive or personal information. Ensuring data privacy and security while using AI is critical and challenging.

  • Regulatory Compliance: As AI technologies evolve, regulatory frameworks may lag, creating uncertainty about compliance requirements. Auditors need to stay informed about changing regulations and ensure that AI applications comply with all relevant laws.


Examples of Success When Integrating Artificial Intelligence in Internal Audit Processes


Integrating AI into Internal Audit processes can lead to significant improvements in efficiency, accuracy, and risk management. Here are three examples of organizations that have successfully implemented AI in their Internal Audit functions:


Example 1: JPMorgan Chase Enhances Fraud Detection


Situation: JPMorgan Chase, one of the largest financial institutions in the world, faced challenges in detecting and preventing fraudulent transactions due to the sheer volume of transactions processed daily.

Actions Taken:


  • Implementation of AI-Powered Analytics: JPMorgan Chase implemented AI-driven analytics tools to monitor transactions in real-time. Machine learning algorithms were trained on historical transaction data to identify patterns and anomalies indicative of fraud.

  • Automated Alerts: The system was configured to generate automated alerts for transactions that deviated from established norms, enabling rapid response and investigation.


Outcome:


  • Increased Detection Rate: The financial institution saw a significant increase in the detection rate of fraudulent transactions. AI identified complex fraud schemes that traditional methods missed.

  • Reduced False Positives: The precision of AI algorithms reduced the number of false positives, streamlining the investigation process and improving efficiency.

  • Enhanced Compliance: JPMorgan Chase enhanced its compliance with regulatory requirements by demonstrating robust fraud detection and prevention mechanisms.


Example 2: General Electric Optimizes Risk Management


Situation: General Electric, a global manufacturing conglomerate, struggled with effectively assessing and managing operational risks across its extensive supply chain.


Actions Taken:


  • AI-Based Risk Assessment: GE deployed AI tools to analyze data from various sources, including supply chain logistics, production data, and market trends. Machine learning models were used to predict potential risks and disruptions.

  • Predictive Maintenance: AI was utilized to implement predictive maintenance for critical machinery, using sensors and historical data to forecast equipment failures and schedule timely maintenance.


Outcome:


  • Improved Risk Mitigation: The AI-driven risk assessment provided early warnings of potential disruptions, allowing GE to mitigate risks proactively.

  • Cost Savings: Predictive maintenance reduced unplanned downtime and maintenance costs, leading to significant operational savings.

  • Operational Efficiency: The integration of AI optimized supply chain management, improving overall operational efficiency and resilience.


Example 3: Walmart Enhances Audit Accuracy and Efficiency


Situation: Walmart, the world's largest retailer, faced difficulties in conducting timely and accurate internal audits across its numerous stores due to the large volume of transactions and data.


Actions Taken:


  • AI-Driven Audit Automation: Walmart implemented AI tools to automate the data collection and analysis process for internal audits. Natural language processing (NLP) was used to analyze and extract relevant information from unstructured data such as emails and documents.

  • Anomaly Detection: Machine learning algorithms were employed to identify anomalies and irregularities in financial transactions and inventory records.


Outcome:


  • Increased Audit Efficiency: The automation of routine audit tasks significantly reduced the time required to complete audits, allowing the Internal Audit team to focus on high-value activities.

  • Enhanced Accuracy: AI-driven anomaly detection improved the accuracy of audits by identifying discrepancies that manual processes overlooked.

  • Actionable Insights: Walmart gained actionable insights into operational inefficiencies and areas for improvement, leading to better decision-making and strategic planning.



Conclusion


Artificial Intelligence is transforming the field of Internal Audit by automating routine tasks, enhancing data analysis, and improving the accuracy of audit findings. While AI will not replace auditors, it will enhance their capabilities, allowing them to focus on more strategic and judgment-based tasks. Generative AI tools like ChatGPT, Copilot, and Gemini offer significant benefits but also pose challenges that require careful management. By leveraging AI effectively and addressing its limitations, Internal Auditors can significantly enhance their impact and contribute to more robust and reliable audit processes.

When, “Let’s Soften This” becomes an Auditor’s Risk

The conversation is almost always polite and rational: “The issue is being addressed.” “This could trigger unnecessary regulatory questions.”Let us deal with it operationally first.”

I have been in this conversation more times than I can count and in the moment it always sounds reasonable and common sense. That is what makes it dangerous.

This is not an abstract ethics debate, but a recurring challenge, particularly in highly regulated environments like banking, insurance or aviation. It carries a risk that is rarely named. I think of it as Deferred Accountability Risk.

Why this pressure arises and why auditors hesitate

In my experience, management pressure to soften audit reporting usually comes from understandable places.

There is often genuine regulatory anxiety. Senior executives know that audit reports can be accessed by regulators and read without context. Even balanced findings can look stark when lifted out of a broader discussion, so the instinct to manage the written record is not irrational.

There is also reputational concern. Once something is formally escalated to the Audit Committee, it becomes part of the governance record and cannot be unseen or quietly resolved. Softening language is often presented as buying time to fix the issue before it attracts attention or escalates beyond proportion.

There is also the reality of working relationships. Internal audit operates inside the organisation. Escalation can be portrayed as being rigid or disconnected from operational realities, even when the concern itself is valid.

None of this makes management unreasonable. In fact, this is precisely why auditors pause. The request appeals to pragmatism rather than compromise. The problem is that what feels like pragmatism at the time can quietly change where accountability sits.

What Deferred Accountability really looks like in practice

Deferred accountability risk arises when an organisation chooses to delay formal recognition of an issue in order to avoid immediate discomfort or scrutiny. Accountability does not vanish. It waits.

What often goes unspoken in these moments is what management is functionally asking internal audit to do. When management asks audit to soften or omit an issue from a report, they are not just asking for a different turn of phrase. They are asking audit to step slightly outside its assurance role and carry part of management’s accountability for how and when the issue is formally acknowledged.

That shift matters and at that point, the audit report stops being a mirror and starts becoming a shield for management.

While the exposure may not be obvious immediately, it becomes very obvious later, when questions are asked about who knew what, and when.

What history keeps showing us

Large corporate failures are often described as sudden but they rarely are. More often, they follow a slow, familiar pattern where issues are raised, explanations are accepted, discomfort is managed and escalation is delayed.

The sales practices scandal at Wells Fargo is a case I keep returning to. What struck me most when revisiting the timeline was not the scale of the misconduct, but how early the warning signs appeared. Internal concerns existed for years. What failed was not awareness, but escalation with enough clarity and persistence to force governance attention. When accountability finally arrived, it came through regulators, and by then the damage was extensive.

This pattern is not confined to one jurisdiction.

In India, the collapse of Satyam Computer Services showed how comfort can gradually replace verification. Cash balances were accepted rather than aggressively challenged. Each reporting cycle deferred the reckoning. When accountability arrived, it arrived publicly and painfully, with consequences that extended beyond the company itself.

In South Africa, Steinhoff International followed a similar trajectory. Complex structures produced plausible explanations. Concerns were absorbed rather than escalated. Oversight mechanisms existed, but they did not interrupt the pattern. The eventual collapse wiped out billions in value and directly affected pension funds and ordinary investors.

In none of these cases was accountability avoided. It was postponed, and the cost of that postponement compounded over time.

In these cases, early warning signs and governance red flags were present for years before decisive action occurred — whether through internal controls, risk reporting, or escalation to governance bodies — underscoring how accountability was continually deferred until regulators intervened or crises erupted.

Why agreeing to soften can feel like the sensible option

From the auditor’s point of view, agreeing to soften language can feel like a reasonable compromise. The issue is known. Management appears engaged. The relationship is preserved. The auditor retains influence.

The difficulty is that audit reports are not just internal communications. They are governance artefacts. What is included, excluded, or diluted shapes what the Audit Committee knows and when it knows it.

Once something is left out of the formal record, bringing it back later is rarely straightforward. Auditors often underestimate how irreversible these decisions are.

From an Audit Committee perspective, the question after a failure is not whether management felt pressured or whether wording was polite. It is whether the committee had enough information, early enough, to act.

That is where deferred accountability becomes personal.

How I have seen experienced auditors think it through

The most effective auditors I have worked with do not treat this as a binary choice between compliance and confrontation. They ask themselves uncomfortable questions.

·       How would this decision read if examined in hindsight? Not by colleagues, but by a regulator or inquiry that does not know the personalities involved.

·       Am I, by softening this, making a judgement on timing that properly belongs to the Audit Committee?

·       Am I assuming this can be revisited later, when in reality that may be much harder than it sounds?

·       Is this an isolated accommodation, or one more small adjustment in what will become a pattern?

These are not checklist questions. They are judgement calls, but they change the nature of the conversation.

Navigating the issue without burning bridges

Independence does not require theatrics. It requires clarity.

In practice, experienced Chief Audit Executives often separate recognition from remediation. They ensure that the issue and its implications are clearly articulated for the Audit Committee, while still acknowledging management’s response and context.

They stay anchored to a simple reality. While day-to-day interactions may be with management, internal audit ultimately exists to support the Audit Committee’s oversight responsibilities. When in doubt, they ensure the committee has enough information to make its own call.

That stance does not always make life easier. It does tend to build long-term credibility.

The cost of getting this wrong

The cost of deferred accountability is rarely borne by those who ask for softening in the moment. It is borne later, often by people who had no part in the original conversation.

Post-incident reviews do not dwell on tone. They focus on whether warning signs were surfaced, whether escalation was timely, and whether governance bodies were adequately informed.

I have yet to see an inquiry conclude that an organisation failed because an audit report was too clear.

A final reflection

Internal audit adds the most value when it surfaces accountability while it is still manageable.

When reporting is diluted to preserve short-term harmony, organisations do not eliminate risk. They merely decide when and how accountability will arrive, often without realising they have made that choice.

Deferred Accountability Risk is not about idealism or confrontation. It is about stewardship. Some of the most consequential audit judgements are not about what we find, but how clearly and when we choose to say it.

Navin Pasricha, a former CAE, CRO and Audit Committee Member, is author of,Getting Ready to Roar: Chief Auditor’s Guide from Audit Room to Board Room.”


Whistleblower Protection and Anti-Retaliation: Strengthening Trust, Governance, and Organizational Resilience

Globally, legislation such as the EU Whistleblower Protection Directive, SOX Section 806, and various regulatory guidelines emphasize that robust anti-retaliation frameworks are essential to maintaining trust, promoting transparency, and reducing external exposure.

This article examines practical aspects of building and maintaining an environment where employees can speak up without fear. It draws on regulatory requirements, lessons learned from real-world investigations, and common audit findings in multinational environments. A dedicated section also outlines how Internal Audit can structure an effective review of whistleblower and anti-retaliation programs.

  1. Why Trusted Reporting Channels Matter

    In theory, most organizations say they value transparency. In practice, employees often decide whether to speak up based on how they’ve seen issues handled before. A channel may be available, but if employees believe their complaint will be ignored—or worse, backfire—silence becomes the safer option. A reliable reporting structure is marked by:  clear, visible communication on how to report issues;  access to confidential and, where allowed, anonymous channels;  a predictable process after the report is submitted;  a demonstrated history of respecting confidentiality and acting on concerns. From an auditor’s perspective, one of the most consistent red flags is not the absence of a channel, but a channel that exists only on paper, with low usage, unclear ownership, or slow follow-up.


  2. Global Expectations and Legal Obligations

    Around the world, regulations increasingly require not just internal channels, but concrete protections against retaliation. Examples include: 

    • EU Whistleblower Protection Directive, which mandates structured reporting channels and clear protection mechanisms; 

    • SOX Section 806, which protects employees of publicly traded companies in the U.S.;

    • OSHA programs, which enforce whistleblower protections across multiple federal statutes; 

    • Brazil’s Anti-Corruption frameworks, which encourage companies to adopt internal reporting mechanisms as part of integrity systems.


    While the legal requirements vary, the underlying expectation is consistent: organizations must actively prevent retaliation and ensure confidentiality. Regulators no longer accept passive or symbolic compliance.


  3. The Real Impact of Retaliation

    Retaliation rarely appears as an explicit threat. More often, it appears subtly: 

  • a sudden change in project assignments; 

  • exclusion from meetings; 

  • a shift in the manager’s tone; 

  • performance evaluations that no longer reflect the employee’s actual work;

  • social or professional isolation.

These behaviors may seem minor in isolation, but for the employee involved, they can become deeply discouraging—and visible to colleagues. Internal Investigations and Audit functions often identify that retaliation, or the fear of it, spreads through informal channels long before it reaches formal ones. The result is predictable: employees stop reporting, risks increase, and leadership loses visibility into emerging problems.

  1. Building an Effective Anti-Retaliation Environment

Organizations that take whistleblower protection seriously tend to focus on five practical pillars:


1. Credible Leadership Employees watch how leaders behave, not what is written in a policy. When executives and managers acknowledge concerns respectfully, escalate issues properly, and avoid “shooting the messenger,” the entire program gains credibility.


2. Clear and Accessible Policies Policies should be written in plain, direct language. They must explain: 


  • what retaliation is (with examples); 

  • what behaviors are prohibited; 

  • reporting channels available; 

  • how confidentiality is handled; 

  • what employees can expect after submitting a concern.


A well-written policy is often one of the first controls Internal Audit reviews.


3. Investigation Quality and Consistency One of the fastest ways to damage trust is to mishandle an investigation. Leading practices include:  a consistent intake and triage process;  conflict-of-interest checks;  investigators who are trained and independent;  timely communication with the whistleblower (where permissible under law);  documentation that enables auditability.


4. Training That Resonates Annual training is useful—but insufficient. Supervisors in particular need practical guidance on:  recognizing subtle retaliation;  handling concerns neutrally;  protecting confidentiality;  escalating issues properly. A surprising number of retaliation cases occur because a supervisor acted defensively or impulsively—not maliciously—after an employee raised a concern.

5. Oversight, Metrics, and Data Organizations should track trends such as:  volume and type of reports;  case cycle time;  number of retaliation allegations;  outcomes of disciplinary measures;  employee sentiment surveys. When Internal Audit later performs its assessment, this dataset becomes crucial.

  1. Transparency and Follow-Through

    Employees judge the seriousness of a whistleblower program by what happens after the report is filed. While confidentiality is essential, organizations can still communicate:  that issues are taken seriously;  that corrective actions were implemented;  that retaliation is not tolerated;  that the company is willing to fix systemic weaknesses. A practical example: Some companies publish anonymized case summaries or annual ethics reports. This small step significantly improves trust because employees see evidence that reporting leads to real action.


  • What Leaders Must Do to Protect Whistleblowers

    Protecting whistleblowers is not only a compliance task—it is a leadership responsibility. Effective leaders:  create psychological safety;  intervene quickly when they perceive retaliatory behavior;  avoid discussing the reporter’s identity, even informally;  reinforce that speaking up is part of risk management, not disloyalty;  hold managers accountable for retaliation through performance metrics. These behaviors send a clear message: retaliation is inconsistent with the company’s values and will be addressed.


How Internal Audit Should Review Whistleblower and Anti-Retaliation Programs

An Internal Audit review provides independent assurance that the whistleblower framework is effective in practice—not just compliant on paper.


Audit Objective

To assess whether whistleblower reporting channels, investigations, and anti-retaliation measures are well-designed, properly implemented, and operating effectively.


Key Audit Areas

1. Governance and Oversight 

  • Review the role of Compliance, HR, Legal, and the Audit Committee. 

  • Assess reporting lines and independence. 

  • Examine dashboards, KPIs, and board reporting.


2. Policy Review 

  • Evaluate clarity, accessibility, and alignment with relevant regulations. 

  • Confirm the presence of anti-retaliation language that is actionable, not symbolic.


3. Reporting Mechanisms 

  • Test availability, confidentiality, and response times. 

  • Review vendor controls if a third-party hotline is used.


4. Investigation Management Audit should examine: 

  • intake and triage processes; 

  • documentation quality; 

  • timeliness; 

  • conflict-of-interest controls; 

  • communication practices with reporters.


This area often reveals inconsistencies between regions or business units.


5. Retaliation Monitoring Internal Audit should verify: 

  • whether whistleblowers receive follow-up checks; 

  • how retaliation allegations are triaged and investigated; 

  • whether corrective actions are tracked.


6. Culture and Tone While harder to measure, Internal Audit can interview employees, review survey data, and analyze turnover in sensitive areas. This qualitative insight often reveals the most valuable information.


Deliverables

The audit report should include: 

  • a risk-based overall rating; 

  • findings with root-cause analysis; 

  • remediation recommendations; 

  • suggestions for cultural improvements; 

  • opportunities to strengthen monitoring and data analytics.


Conclusion

Whistleblower protection and retaliation prevention are no longer optional features of modern compliance systems—they are strategic investments in organizational resilience. Companies that proactively safeguard whistleblowers strengthen their risk-intelligence capabilities, reduce regulatory exposure, and promote a culture of ethical transparency. Internal Audit plays an essential role by independently assessing whether programs are effectively designed and operating as intended. When organizations integrate strong reporting channels, clear policies, committed leadership, and structured oversight, they build a robust environment where employees feel safe to speak up—allowing the organization to detect issues early and maintain long-term integrity.


Endnotes

1. Transparency International. Protecting Whistleblowers: Best Practices and Key Principles.

2. NAVEX Global. Regional Whistleblowing Benchmark Report.

3. Occupational Safety and Health Administration (OSHA). Whistleblower Protection Program.

4. European Union Directive 2019/1937 on the Protection of Persons Who Report Breaches of Union Law.

5. Sarbanes-Oxley Act of 2002 (SOX), Section 806 – Employee Protection Provisions.


Author

Douglas Siedler Rodrigues Pedroso is an Internal Audit and Investigations executive with extensive experience in Latin America and the U.S., including Fortune 500 environments. He has led over 200 investigations, implemente SOX frameworks in multiple organizations, and is recognized for enhancing governance and risk management through audit-driven insights.

The Impact of the Organisational Culture on the Internal Audit Function

As the Institute of Directors notes, an organisation’s culture comprises shared values, beliefs, and assumptions about how employees behave and interact at work (IoD, 2024). According to the IIA, all organisations have a culture, whether intentionally created or not. This includes potential subcultures within an organisation, which are most likely to occur in a geographically widespread organisation. Although Internal Audit generally falls under the third line of defence in most organisations, Internal Auditors are still employees of the organisations and are influenced by the organisation’s culture (IIA, 2024). The question is to what extent the organisation’s culture impacts the effectiveness of an independent Internal Audit.


Purpose of Internal Audit

Internal Audit (IA) has been defined as an independent, objective assurance and advisory service designed to add value and improve an organisation’s operations. It helps an organisation accomplish its objectives by bringing a systematic disciplined approach to evaluate and improve the effectiveness of governance, risk management, and control processes (IIA, 2024).


Per the definition, the IA function is to provide value and to improve an organisation’s operations. How the value provided and the improvements recommended are taken on depends on the organisation's culture.


Work Culture

An organisation can have a positive or negative work culture. A negative work culture is colloquially referred to as a toxic work culture. Per the definition, “Culture represents the invisible belief systems, values, norms, and preferences of the individuals that form an organisation. Conduct represents the tangible manifestation of culture through the actions, behaviours, and decisions of these individuals.” (St-Onge et al, 2018)


A positive work culture can be indicative that an organisation has strong core values. Organisations may predefine their core values as part of their mission and vision, outlining the type of organisation they aim to be and what they aim to achieve. Core values may be promoted by senior leadership and driven down through the organisation's governance; however, it is employees who ultimately drive the organisation's core values forward. Core values reflect the values employees bring to the work environment, shaped by their worldviews, beliefs, and experiences. It is then essential that leadership is selective in the employees they hire to ensure a good cultural fit in the organisation. Cultural fit occurs when employees feel connected to the organisation's values and understand the business's needs. Good cultural fit ensures staff retention, employee engagement, performance and productivity, team cohesion, and strong communication (Benstead, 2023). In today’s highly competitive environment, it is clear that employees’ expectations of organisations are closely tied to their values. For organisations that deliver on an employee’s expectations, this results in more loyal and productive employees, ultimately leading to organisational success (Laker, 2021).


A negative work culture is where behaviours such as manipulation and bullying are innate to the culture of the organisation, where low productivity, lack of trust, high stress levels, infighting and discrimination become the norm, resulting in employees feeling psychologically unsafe (Sandhu, 2024). A negative work culture, if left unmanaged, can spread quickly, like a rotten apple in a barrel. A negative work culture has become a significant concern for many organisations, as their culture can sway the organisation's success. Some signs of a toxic work culture include unfair treatment or discrimination, limited growth opportunities, a lack of workplace trust and support among team members, excessive workload and unrealistic expectations, poor communication and lack of transparency, ineffective leadership, and high employee turnover, to name a few (Hastwell, 2023). Although companies may develop a toxic work culture, there are ways to improve and foster a positive one again. For leadership, such steps would include rebuilding trust in the organisation. With organisations taking accountability for past mistakes and current issues, and staying consistent in the right places for employees, a positive work culture can be rebolstered. (Hastwell, 2023)


Impact on Internal Audit

It is sensible to assume that in a positive work culture environment, the value and insights provided by IA to an organisation would be accepted and improvements implemented following observations raised by IA.


However, within a toxic work culture, IA may experience one or more threats to their independence. A toxic work culture hinders and undermines the IA by deterring honest reporting, increasing burnout, fostering cover-ups, and making the IA unwilling to challenge leadership or report real risks due to potential retaliation. This, in turn, may create a culture of silence and check box auditing rather than proper risk management, exposing IA to governance and fraud risks (Flying colour, 2023). As outlined in the IIA's Auditing Culture Global Practice Guide, a toxic organisational culture corrodes the effectiveness of an organisation's controls (IIA, 2024).


An organisation's culture is the responsibility of the board of directors and senior leadership, as is the management of the organisation's risks and controls. Highlighting their role can empower leaders to feel confident in their ability to shape a positive environment. It is therefore the responsibility of the board of directors and senior leadership to set the tone at the top for the implementation of a positive work culture (IIA, 2024).


The board of directors and senior leadership may request that IA provide insight into an organisation's cultural temperature through an assurance or advisory engagement. Emphasising the value of these insights can motivate leaders to seek and trust IA's input on cultural matters actively. Such insights can include identifying the root causes of the development of a toxic work culture, assessing the governance structure related to culture, assessing the modes and means of communication in the organisation, and assessing the organisation's culture-related training, to name a few. (IIA, 2024)


Conclusion

Ultimately, organisational culture plays a pivotal role in determining whether IA can operate effectively and deliver meaningful value. While the responsibility for establishing and maintaining a positive culture rests with the board of directors and senior leadership, IA has a critical role in providing independent insight into cultural strengths and weaknesses. A positive culture enables open challenge, supports auditor independence, and reinforces robust governance and risk management. In opposition, where a toxic culture exists, both the organisation and the IA function are exposed to increased governance, compliance, and fraud risks. By proactively assessing cultural drivers and communicating open observations, IA can support leadership in addressing underlying issues and strengthening the control environment, thereby safeguarding the organisation’s long‑term resilience and integrity.


References:

·       Institute of Directors (IoD), 2024,  Organisational culture | Factsheets | IoD

·       IIA, 2024, Auditing Culture, Global Practice Guide: Auditing Culture, 2nd Edition | The IIA

·       IIA, 2024, Global Internal Audit Standards 2024, Complete Global Internal Audit Standards

·       St-Onge, Elizabeth, Ege Gürdeniz, and Elena Belov. Measuring Conduct and Culture: A How-To Guide for Executives. New York: Oliver Wyman, 2018. the-relationship-between-organizational-culture-and-turnover-intention-a-literature-review-study.pdf

·       Harvard, 2023,  Why Workplace Culture Matters - Professional & Executive Development | Harvard DCE

·       Benstead, S., 2023, Breathe,  What is cultural fit and why is it important? | Breathe Blog

·       Laker, B., 2021, Forbes,  Culture Is A Company’s Single Most Powerful Advantage. Here’s Why

·       Sandhu, P., 2024, The Muse, 9 Signs You’re in a Toxic Work Environment—and What to Do About It | The Muse | The Muse

·       Hastwell, C., 2023, Great place to work 8 Signs of Toxic Company Culture That Drive Employee Turnover | Great Place To Work®

·       Flying Colour, 2023, Importance of Auditor’s Independence, Threats and Consequences of Compromised Independence - Flying Colour Tax

Editor’s Introduction — Edition 1, Q1, 2026

The Internal Audit Review has been created at a time when the internal audit profession is undergoing profound change. Expectations placed on internal auditors have expanded rapidly, while the risk landscape has grown more complex, interconnected, and less predictable. Traditional models of assurance, while still essential, are no longer sufficient on their own. Internal audit is increasingly expected to provide insight, foresight, and perspective — to help organisations navigate uncertainty rather than merely confirm compliance.

This publication exists to support that evolution.

Why Internal Audit Review, and Why Now

Across industries and sectors, internal audit functions are being asked to do more with less, to cover broader risk universes, and to operate at greater strategic altitude. Digital transformation, cyber risk, regulatory change, data integrity, environmental and social responsibility, and geopolitical volatility have all reshaped the assurance agenda. At the same time, audit committees and executive management expect internal audit to be relevant, timely, and forward-looking.

Yet many practitioners experience this shift in isolation — grappling with new expectations without always having access to practical insight, peer learning, or space for thoughtful reflection. Internal Audit Review was conceived in response to that gap.

This journal is intended to be a place where ideas can be explored with depth, where emerging challenges can be examined critically, and where professional judgement is valued as much as technical compliance. It is not designed to replicate standards or restate guidance already available elsewhere. Instead, it seeks to complement them by focusing on interpretation, application, and lived experience within the profession.

A Quarterly Space for Reflection and Insight

As a quarterly publication, Internal Audit Review is deliberately paced. In a world saturated with rapid commentary and fleeting opinion, there remains a need for considered analysis — writing that allows time to reflect, connect themes, and extract meaning.

Each edition will focus on issues shaping the present and future of internal audit, drawing on contributions from practitioners, leaders, and subject-matter experts. Articles will range from strategic perspectives and thematic analysis to practical insights grounded in real-world audit environments. Over time, the Review aims to build a body of work that reflects the maturity, diversity, and evolving nature of the profession.

This first edition sets the tone. It marks the beginning of an ongoing conversation — one that will develop, deepen, and broaden with each subsequent issue.

Beyond a Journal: An Evolving Initiative

While Internal Audit Review takes shape initially as a publication, it represents only one part of a broader initiative.

As we move through 2026 and beyond, the Internal Audit Review initiative will expand with a deliberate focus on education, advocacy, and connection.

  • Education, by supporting continuous professional learning through articles, resources, and future learning initiatives that bridge theory and practice.

  • Advocacy, by contributing to informed discussion about the value, independence, and positioning of internal audit within organisations and across society.

  • Connection, by fostering a professional community where auditors can share perspectives, learn from one another, and engage across sectors and geographies.

The intention is not to speak at the profession, but to grow with it — shaped by the challenges practitioners face and the insights they bring.

An Independent and Practitioner-Focused Voice

A defining principle of Internal Audit Review is independence — not only in the assurance sense, but in thought. The Review is not aligned to any single methodology, sector, or commercial interest. Its credibility will rest on the quality of ideas it presents and the integrity of the discussions it hosts.

Contributors are encouraged to challenge assumptions, explore grey areas, and reflect honestly on what works, what does not, and what remains unresolved. Internal audit does not exist in a vacuum, and neither should the conversations that shape it.

By creating space for thoughtful, sometimes uncomfortable, but always constructive dialogue, Internal Audit Review aims to contribute meaningfully to the profession’s long-term development.

An Invitation to Engage

This first edition is both a beginning and a call to action.

Whether you are an experienced chief audit executive, a developing practitioner, a risk or governance professional, or someone with an interest in assurance and organisational resilience, you are invited to engage with Internal Audit Review. Read critically. Reflect openly. Contribute generously.

Future editions will be strengthened by diverse voices and perspectives — from different industries, regions, and career stages. The success of this initiative will not be measured solely by readership, but by the quality of conversation it enables and the professional confidence it helps to build.

Looking Ahead

Internal audit has always been a profession grounded in judgement, ethics, and public trust. As its role continues to evolve, so too must the ways in which we learn, share, and lead.

Internal Audit Review is committed to being part of that evolution — not as a definitive authority, but as a trusted forum for insight, discussion, and connection.

Thank you for being part of this first edition. I look forward to the dialogue ahead.

Thomas Bullman
Founder and Executive Director
Internal Audit Review

New Certified Internal Audit Exam - What has changed?

This article can be interesting for CIAs who earned thier titles based on former syllabi so as to be able to give information current candidates in case of any questions arises.


Why the CIA Exam Changed in 2025?

The Institute of Internal Auditors (IIA) revamped the CIA exam to:

·       Reflect modern global internal audit practices

·       Align with the new Global Internal Audit Standards™ (effective January 9, 2025)

·       Reduce redundancy across the three exam parts

·       Clarify required knowledge and skills for candidates

The 2025 CIA exam update isn’t just a refresh based on new Global Audit Standards —it’s a redefinition.


Transition Rules You Should Know

Rule 1: English exam transitions was on May 28, 2025. Other languages follow a staggered schedule (e.g., Japanese: July 28, Spanish: October 28, etc.)

Rule 2: Passed parts under the old syllabus remain valid for 3 years from registration. If you passed any part before the new syllabus launches, you don’t need to retake it under the new format—unless your program expires. You can mix new parts with formerly passed parts (e.g., Part 1 & 2 before May 28, 2025 + Part 3 after) and still earn the CIA designation. Several other combinations are possible if we consider the languages as well. But to buy new learning materials is highly recommended

No Prescribed Order: You can take the parts in any order. The IIA doesn’t require a specific sequence.


Part 1: Internal Audit Fundamentals

The name of Exam Part 1 was changed from "Essentials of Internal Auditing" to "Internal Audit Fundamentals". There were moderate adjustments, mainly refining foundational concepts. It can be said that Part 1 has changed the least in the entire CIA exam system; in other words, it has the most in common with the old syllabus.


Foundations

Syllabus 2019

Syllabus 2025

I. Foundations of Internal Auditing 15 %

A) Foundations of Internal Auditing 35 %


The weight of this unit has apparently increased significantly; however, this does not necessarily mean that the fundamentals will be scrutinized much more deeply. Rather, it is a consolidation of core Standard components based on the new system. Crucially, quite a few subunits from the former Part 2 (such as the types of Assurance and Advisory Services) have been moved here. This consolidation is visually indicated by the blue-to-gray color transition in the pie chart, showing that the section is composed of more than just its original content.


Independence, ethics, professionalism

Syllabus 2019

Syllabus 2025

II. Independence & Objectivity 15 %

B) Ethics & Professionalism 20 %

III. Proficiency & Due Professional Care 18%


Two main units, which were closely related anyway, were merged into one. Overall, their combined weight decreased from 33% to 20%. This reduction does not mean that the importance of these units has diminished. These principles will also arise later in several other practical issues in Part 2 and Part 3.


QAIP moved to Part 3

Syllabus 2019

Syllabus 2025

IV. Quality Assurance and Improvement Program

Moved to Part 3


This unit was entirely moved to Part 3 (Internal Audit Function). This strategic shift highlights that the overall management and governance of the internal audit function, including QAIP, is now considered a strategic, leadership-level responsibility, appropriate for the redefined Part 3 syllabus.


GRC and Fraud Risk

Syllabus 2019

Syllabus 2025

V. Governance, Risk Management, Control 35 %

C) Governance, Risk Management, Control 30 %

VI. Fraud Risk 10 %

D) Fraud Risk 15 %


There were no significant thematic changes to GRC, just a minor adjustment in weight. Conversely, the weight assigned to Fraud Risk increased, indicating a growing emphasis on understanding the basic principles of fraud schemes and detection even at the foundational level of the CIA exam.


Part 2: Internal Audit Engagement

The name of Exam Part 2 was changed from „Practice of Internal Auditing” to „Internal Audit Engagement”. While this content underwent significant restructuring—arguably more so than Part 1, but certainly less than Part 3 — this overhaul is visually summarized in the figure below.



Managing the IAA moved to Part 1 & 3.

Syllabus 2019

Syllabus 2025

I. Managing the Internal Audit Activity 20 %

Moved to Part 1

Moved to Part 3 and split into two units


This unit was eliminated from Part 2, but Its content was redistributed across the other two exam parts:

·       New Part 1: As we discussed in the previous article concerning Part 1 changes, several subunits from the former Part 2 (such as the types of Assurance and Advisory Services) have been relocated to Part 1.

·       New Part 3: Other subunits from this eliminated unit were moved to Part 3, which itself was subsequently split into two distinct Units. We will provide a detailed analysis of these specific changes in the article dedicated to Part 3 revisions.


Planning

Syllabus 2019

Syllabus 2025

II. Planning the Engagement 20 %

A) Engagment Planning 50 %


Frequently, the professional community debated whether the elimination of the former Part 3, "Business Knowledge for Internal Auditing," which was widely considered the most difficult of the three parts, would lead to an excessively simplified path to achieving the Certified Internal Auditor (CIA) designation. A closer examination of the curriculum, moving beyond the mere unit and subunit titles, reveals that this assumption is inaccurate. In addition, the unit's weighting dramatically increased from 20% to a substantial 50%.

While the former content of Engagement Planning has largely remained consistent, the dramatic increase in weighting is due to the integration of several critical subunits (Basic Acumen as well as IT related units) migrated from the former Part 3 (Business Knowledge for Internal Auditing).

What is the fundamental conclusion we can draw from this restructuring? The former Part 3 material was arguably somewhat disjointed or isolated within the curriculum. Now, some of the previous Part 3 units have been directly linked to the practical components of the exam. While the weighting of these specific units may appear reduced as they are now embedded within other exam parts, the overall workload and complexity for the candidates have certainly not decreased.

Performing

Syllabus 2019

Syllabus 2025

III. Performing the Engagement 40 %

B) Information Gathering, Analysis and Evaluation 40 %


The weight of this section remained constant at 40%. Although the subunit has been renamed, its content has not changed significantly. Consequently, most study materials are expected to feature very similar chapters on this unit.


Communication and Monitoring – stayed and/or moved to Part 3

Syllabus 2019

Syllabus 2025

IV. Communicating Engagement and Monitoring Progress 20 %

C) Engagement Supervision & Communication 10 %

Moved to Part 3


The content of the former Part 2 subunit was effectively split into two components:

·       New Part 2: Some core elements were retained in the new Part 2 syllabus under unit C.

·       New Part 3: Most of the key elements, particularly those related to Recommendations and Monitoring, have been relocated to the new Part 3 exam Unit D.

This split unit now accounts for 10% of the new Part 2 exam, while the relocated elements constitute 45% of the new Part 3 exam. This combined weighting significantly surpasses the former Part 2's weighting for these activities (which was 20 %), ultimately indicating an overall increased importance placed on the Value Added Activity of Internal Auditing.


Part 3: Functions of Internal Auditing

The new Part 3 is almost entirely different from its predecessor. The former Part 3 was entitled "Business Knowledge for Internal Auditing". The new Part 3 fundamentally represents a consolidation of the former Part 1 and Part 2 Units that focused specifically on the management activities of the Chief Audit Executive (CAE). By aggregating these key leadership responsibilities into a dedicated exam part, the unit of Internal Audit Management now receives significantly greater attention and weighting compared to previous syllabi.

The summary of these massive changes is illustrated in the figure below, which clearly reflects the scale of this restructuring.



The Fate of Former Part 3 Units

Syllabus 2019

Syllabus 2025

I. Business Acumen 35 %

Partially moved to Part 2

Small subunits stayed in Part 3

Partially eliminated

II. Information security 25 %

III. Information Technology 20 %

IV.  Financial Management 20 %


Its four main units have not been totally eliminated from the CIA exams, but have been strategically relocated:

·       Several units and subunits moved to the new Part 2 A) Engagement Planning, as discussed.

·       Some smaller subunits retained in the new Part 3 A) Internal Audit Operations (specifically within Resource management).

·       Certain highly specialized areas (Managerial accounting concepts, Costing systems, and specific IT infrastructure concepts) are no longer directly included in the exam syllabus.


Relocation of IAA Management from Part 2

Syllabus 2019

Syllabus 2025

From Part 2 I. Managing the IAA 

A) Internal Audit Operations 25%

B) Internal Audit Plan 15 %


2019 Part 2 I. Managing the Internal Audit Activity VS 2025 Part 3 A) Internal Audit Operation & B) Internal Audit Plan

The former Part 2 Unit was not only moved to the New Part 3 (and Part 1), but was also split into two distinct Units. Collectively, the relocated elements received a greater weighting: increasing from 20% up to 40%. Furthermore, considering some subunits moved to Part 1, the total focus increase on these management units is substantial.


QAIP from Part 1

Syllabus 2019

Syllabus 2025

From Part I. QAIP

C) QAIP 15 %


The weighting of Quality Assurance and Improvement Program (QAIP) has also increased. It was 7% in the former Part 1, but is now weighted at 15% in the new Part 3 syllabus.


Emphasis on Value-Added Outcomes from Part 2

Syllabus 2019

Syllabus 2025

Form Part 2

D) Engagement Results & Monitoring 45 %


As noted, roughly half of this former Part 2 unit was retained in the new Part 2 (at 10%), while the other half was relocated to Part 3. The total weighting of this split section significantly increased from 20% up to 55% (10% in Part 2 and 45% in Part 3). This huge increase indicates that internal auditing must deliver tangible results to add Value to the Organization.

The changes and transitions can be summarized in the following charts:

Conclusion

The overall structure clearly indicates that the CIA program has been strategically reorganized to follow the real-life audit flow: Fundamentals (Part 1) lead to Planning and Execution (Part 2), which culminates in Management and Value-Added Outcomes (Part 3). The result is a more integrated and practically relevant certification designed to prepare auditors for leadership roles.

Is the new system easier or harder for candidates? Which is the hardest Part, and which is the easiest part of the new system. There is no universal answer this question. It depends on each candidates interest and experience.


Sources:

2019 CIA Syllabi

2025 CIA Syllabi

https://www.theiia.org/en/certifications/cia/exam-prep-resources/exam-syllabus/

Gleim CIA products

https://www.gleim.com/cia-review/

Zain Academy CIA products

https://zainacademy.us/product-category/cia-exam-review/


Charts are created by EXCEL and Paint




Transforming Internal Audit: The Role of Artificial Intelligence

These processes include learning (the acquisition of information and rules for using the information), reasoning (using rules to reach approximate or definite conclusions), and self-correction. AI aims to create systems that can perform tasks that would normally require human intelligence, such as visual perception, speech recognition, decision-making, and language translation.


Major Components of Artificial Intelligence


AI is comprised of several key components, each contributing to the development and functioning of intelligent systems. The major components include:


1. Machine Learning (ML)


Machine Learning is a subset of AI that involves the use of algorithms and statistical models to enable systems to improve their performance on a specific task through experience. Rather than being explicitly programmed to perform a task, ML algorithms use data to learn and make decisions.


  • Supervised Learning: Algorithms are trained on labeled data, meaning the input comes with the correct output. The model learns to map inputs to outputs and is evaluated based on its performance on a validation dataset.

  • Unsupervised Learning: Algorithms are used to identify patterns in data without labeled responses. This is useful for clustering data into groups based on similarities.

  • Reinforcement Learning: Algorithms learn to make decisions by receiving rewards or penalties for actions taken, aiming to maximize cumulative reward.


2. Neural Networks


Newral Networks are a series of algorithms that attempt to recognize underlying relationships in a set of data through a process that mimics the way the human brain operates. They are the foundation of deep learning models.


  • Artificial Neural Networks (ANNs): Composed of layers of nodes, or neurons, where each node represents a mathematical function. Data passes through these nodes, enabling the network to learn from data.

  • Convolutional Neural Networks (CNNs): Primarily used in image recognition and processing, CNNs apply convolutional layers to preserve the spatial relationships between pixels.

  • Recurrent Neural Networks (RNNs): Used for sequential data, such as time series or natural language, RNNs have connections that loop back on themselves to maintain a memory of previous inputs.


3. Natural Language Processing


Natural Language Processing (NLP) enables machines to understand, interpret, and generate human language. NLP combines computational linguistics with machine learning to process text and speech data.


  • Text Analysis: Involves parsing and understanding text data, such as sentiment analysis, topic modeling, and named entity recognition.

  • Speech Recognition: Converts spoken language into text.

  • Language Generation: Produces human-like text based on input data, often used in chatbots and virtual assistants.


4. Computer Vision


Computer Vision is a field of AI that enables machines to interpret and make decisions based on visual data from the world.


  • Image Classification: Assigning a label to an entire image based on its contents.

  • Object Detection: Identifying and locating objects within an image.

  • Image Segmentation: Partitioning an image into segments to simplify or change the representation of an image into something more meaningful.


5. Robotics


Robotics integrates AI with mechanical engineering to create machines capable of performing tasks autonomously.


  • Sensing: Robots use sensors to gather information about their environment.

  • Planning: Algorithms determine the best course of action based on the robot's goals.

  • Control: Ensuring the robot can execute the planned actions effectively.


Artificial Intelligence in Internal Audit


AI is fundamentally transforming various industries, and Internal Audit is no exception. The integration of AI into audit processes promises to revolutionize the field by automating repetitive tasks, enhancing data analysis capabilities, and improving the accuracy of audit findings. This article explores how AI is revolutionizing Internal Audit, the role of generative AI tools, and addresses critical questions about the future of auditors in an AI-driven world.


Automation of Routine Audit Processes


1. Automating Data Collection and Sampling:


AI significantly reduces the time auditors spend on repetitive tasks such as data collection and sampling. Traditionally, auditors manually gather data from various sources, a process that is not only time-consuming but also prone to human error. AI systems can automate these tasks, efficiently extracting data from multiple sources, including structured databases and unstructured documents.


  • Efficiency: AI tools can process vast amounts of data in seconds, which would take humans days or even weeks.

  • Accuracy: By minimizing human intervention, AI reduces the risk of errors in data collection and sampling, ensuring more accurate audit results.

  • Consistency: AI ensures that data is collected consistently across all audits, improving the reliability of the audit process.


2. AI-Driven Data Analytics for Identifying Anomalies and Patterns:


AI excels in data analysis, particularly in identifying anomalies and patterns that might indicate risks or irregularities. Machine learning algorithms can analyze historical data to establish norms and detect deviations that warrant further investigation.


  • Anomaly Detection: AI algorithms can identify unusual transactions or patterns that could signify fraudulent activities or errors. This capability is crucial for early detection and prevention.

  • Predictive Analytics: AI can predict potential risks by analyzing trends and historical data, allowing auditors to focus on areas with the highest risk.

  • Comprehensive Analysis: AI can handle complex datasets and perform multifaceted analyses, providing deeper insights that traditional methods might miss.


3. Enhancements in Fraud Detection and Risk Assessment:


AI enhances fraud detection and risk assessment by using advanced techniques such as natural language processing (NLP) and machine learning.


  • Real-Time Monitoring: AI systems can continuously monitor transactions and activities, providing real-time alerts for suspicious activities.

  • Risk Scoring: AI can assign risk scores to transactions or entities based on predefined criteria, helping auditors prioritize their efforts.

  • Sentiment Analysis: NLP can analyze communication patterns and sentiments in emails and other documents to detect potential red flags.


Will Artificial Intelligence Replace the Auditor?


While AI offers numerous benefits, it raises the question: Will AI replace the auditor? The consensus among experts is that AI will not replace auditors but rather augment their capabilities.


  • Augmentation Over Replacement: AI handles repetitive and data-intensive tasks, allowing auditors to focus on strategic and judgment-based aspects of the audit. Auditors' expertise in interpreting results, understanding business contexts, and making decisions cannot be fully replicated by AI.

  • New Skill Sets: Auditors will need to develop new skills to work effectively with AI, such as understanding AI outputs, managing AI tools, and interpreting complex data analyses.


The Challenge of Artificial Intelligence "Hallucinations"


AI systems, particularly generative models, can sometimes "hallucinate" and present false information as though it is true. This issue poses a challenge for trust and reliability in AI-driven audits.


  • Understanding Hallucinations: Generative AI models, like ChatGPT, may generate plausible but incorrect information due to biases in training data or inherent limitations in the models.

  • Mitigation Strategies: To mitigate this risk, auditors should cross-verify AI-generated insights with multiple sources and maintain a critical oversight role.


The Role of Generative Artificial Intelligence Tools


Generative AI tools such as ChatGPT, Copilot, and Gemini have the potential to revolutionize the audit landscape, particularly in data analytics.


1. Advantages of Generative AI Tools:


  • Enhanced Data Interpretation: Generative AI can help interpret complex data sets and generate insightful summaries.

  • Automated Reporting: These tools can automate the creation of audit reports, saving time and improving consistency.

  • Interactive Analysis: Generative AI can assist auditors by answering queries in real-time, providing a more interactive and dynamic analysis process.


2. Potential Disadvantages of Generative AI Tools:


  • Accuracy Concerns: The risk of AI-generated misinformation or hallucinations requires careful oversight and validation.

  • Bias and Fairness: AI models can inherit biases from training data, leading to biased outcomes if not properly managed.

  • Dependence on Technology: Over-reliance on AI tools may lead to a decline in auditors’ critical thinking and analytical skills.


Evaluating Artificial Intelligence's Role in the Audit Workflow


Areas Where Generative AI Can Benefit the Audit Workflow:


  • Data Analysis: Enhancing the ability to analyze large datasets quickly and accurately.

  • Report Generation: Streamlining the process of creating detailed and consistent audit reports.

  • Continuous Monitoring: Enabling real-time monitoring and alerting for potential issues.


Areas Where AI Should Be Avoided:


  • Final Judgment: AI should not replace human judgment in making final audit decisions.

  • Ethical Evaluations: Complex ethical considerations and decisions should remain within the purview of human auditors.


Challenges in Integrating Artificial Intelligence in Internal Audit Processes


The integration of AI into Internal Audit processes presents numerous opportunities for efficiency and accuracy but also brings several challenges. These challenges can be broadly categorized into technical, organizational, ethical, and regulatory aspects. Here are some of the key challenges:


1. Technical Challenges


  • Data Quality and Availability: AI systems rely heavily on high-quality, structured data to function effectively. In many organizations, data is often siloed, inconsistent, or incomplete, making it difficult to leverage AI fully.

  • Integration with Existing Systems: Integrating AI tools with existing audit and enterprise systems can be complex and costly. Legacy systems may not be compatible with modern AI technologies, requiring significant upgrades or replacements.

  • Algorithm Transparency and Explainability: AI models, especially those based on deep learning, can be "black boxes," making it difficult for auditors to understand how decisions are made. This lack of transparency can be a significant barrier to trust and acceptance.


2. Organizational Challenges


  • Change Management: Integrating AI into audit processes requires a cultural shift and buy-in from all levels of the organization. Resistance to change from employees accustomed to traditional methods can hinder AI adoption.

  • Skills and Expertise: There is a need for new skills and expertise to manage and work with AI tools. Training auditors to understand and use AI effectively is essential but can be resource-intensive.


3. Ethical and Regulatory Challenges


  • Bias and Fairness: AI systems can inherit biases from the data they are trained on, leading to unfair or discriminatory outcomes. Ensuring that AI operates fairly and ethically is a significant concern.

  • Data Privacy and Security: AI systems often require access to large datasets, which can include sensitive or personal information. Ensuring data privacy and security while using AI is critical and challenging.

  • Regulatory Compliance: As AI technologies evolve, regulatory frameworks may lag, creating uncertainty about compliance requirements. Auditors need to stay informed about changing regulations and ensure that AI applications comply with all relevant laws.


Examples of Success When Integrating Artificial Intelligence in Internal Audit Processes


Integrating AI into Internal Audit processes can lead to significant improvements in efficiency, accuracy, and risk management. Here are three examples of organizations that have successfully implemented AI in their Internal Audit functions:


Example 1: JPMorgan Chase Enhances Fraud Detection


Situation: JPMorgan Chase, one of the largest financial institutions in the world, faced challenges in detecting and preventing fraudulent transactions due to the sheer volume of transactions processed daily.

Actions Taken:


  • Implementation of AI-Powered Analytics: JPMorgan Chase implemented AI-driven analytics tools to monitor transactions in real-time. Machine learning algorithms were trained on historical transaction data to identify patterns and anomalies indicative of fraud.

  • Automated Alerts: The system was configured to generate automated alerts for transactions that deviated from established norms, enabling rapid response and investigation.


Outcome:


  • Increased Detection Rate: The financial institution saw a significant increase in the detection rate of fraudulent transactions. AI identified complex fraud schemes that traditional methods missed.

  • Reduced False Positives: The precision of AI algorithms reduced the number of false positives, streamlining the investigation process and improving efficiency.

  • Enhanced Compliance: JPMorgan Chase enhanced its compliance with regulatory requirements by demonstrating robust fraud detection and prevention mechanisms.


Example 2: General Electric Optimizes Risk Management


Situation: General Electric, a global manufacturing conglomerate, struggled with effectively assessing and managing operational risks across its extensive supply chain.


Actions Taken:


  • AI-Based Risk Assessment: GE deployed AI tools to analyze data from various sources, including supply chain logistics, production data, and market trends. Machine learning models were used to predict potential risks and disruptions.

  • Predictive Maintenance: AI was utilized to implement predictive maintenance for critical machinery, using sensors and historical data to forecast equipment failures and schedule timely maintenance.


Outcome:


  • Improved Risk Mitigation: The AI-driven risk assessment provided early warnings of potential disruptions, allowing GE to mitigate risks proactively.

  • Cost Savings: Predictive maintenance reduced unplanned downtime and maintenance costs, leading to significant operational savings.

  • Operational Efficiency: The integration of AI optimized supply chain management, improving overall operational efficiency and resilience.


Example 3: Walmart Enhances Audit Accuracy and Efficiency


Situation: Walmart, the world's largest retailer, faced difficulties in conducting timely and accurate internal audits across its numerous stores due to the large volume of transactions and data.


Actions Taken:


  • AI-Driven Audit Automation: Walmart implemented AI tools to automate the data collection and analysis process for internal audits. Natural language processing (NLP) was used to analyze and extract relevant information from unstructured data such as emails and documents.

  • Anomaly Detection: Machine learning algorithms were employed to identify anomalies and irregularities in financial transactions and inventory records.


Outcome:


  • Increased Audit Efficiency: The automation of routine audit tasks significantly reduced the time required to complete audits, allowing the Internal Audit team to focus on high-value activities.

  • Enhanced Accuracy: AI-driven anomaly detection improved the accuracy of audits by identifying discrepancies that manual processes overlooked.

  • Actionable Insights: Walmart gained actionable insights into operational inefficiencies and areas for improvement, leading to better decision-making and strategic planning.



Conclusion


Artificial Intelligence is transforming the field of Internal Audit by automating routine tasks, enhancing data analysis, and improving the accuracy of audit findings. While AI will not replace auditors, it will enhance their capabilities, allowing them to focus on more strategic and judgment-based tasks. Generative AI tools like ChatGPT, Copilot, and Gemini offer significant benefits but also pose challenges that require careful management. By leveraging AI effectively and addressing its limitations, Internal Auditors can significantly enhance their impact and contribute to more robust and reliable audit processes.

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Documentation lies at the heart of internal audits, particularly in the area of data protection. While strong controls and processes are vital, auditors rely on documentation to validate whether these practices are consistent, effective, and sustainable. Preparing robust documentation strategies is therefore one of the most critical steps in audit readiness.

The foundation of documentation is a well-structured policy framework. Organizations should ensure that their data protection policies are current, clearly written, and accessible. These policies must cover data classification, access management, incident response, retention, and disposal. Preparing with documented updates demonstrates that the organization not only establishes but also regularly reviews its controls.

Equally important are records of compliance activities. For instance, training logs, risk assessments, breach reports, and vendor due diligence files all provide concrete evidence of compliance. Maintaining these in a centralized and easily retrievable repository ensures auditors can validate claims efficiently.

Data processing registers form another key area. Internal auditors will expect to see detailed records of what personal data is collected, where it is stored, who has access, and how long it is retained. Preparing such registers in advance not only aids audits but also ensures readiness for regulatory inspections.

Change management documentation is often overlooked but highly relevant. Organizations that implement new systems, migrate to cloud platforms, or alter processes must maintain records of privacy assessments, approval workflows, and testing results. Preparing with these records demonstrates a proactive stance toward risk management.

Incident documentation is also crucial. Even organizations with strong defenses face occasional data breaches or near misses. Preparing with detailed incident reports, root cause analyses, and remediation evidence shows auditors that lessons are learned and improvements applied.

To streamline preparation, organizations should establish standardized templates for documenting compliance activities. This consistency reduces errors, saves time, and ensures uniform quality across departments. Automating document management with compliance software can further reduce the administrative burden while improving accuracy.

Finally, organizations should conduct internal reviews of documentation before the audit begins. Verifying completeness, clarity, and accessibility ensures that evidence supports audit findings effectively. It also prevents delays that could arise from missing or disorganized records.

In conclusion, effective documentation strategies transform audit preparation from a reactive scramble into a proactive process. By maintaining policies, compliance records, processing registers, incident logs, and standardized templates, organizations strengthen their data protection audits and build resilience against regulatory scrutiny.

Documentation lies at the heart of internal audits, particularly in the area of data protection. While strong controls and processes are vital, auditors rely on documentation to validate whether these practices are consistent, effective, and sustainable. Preparing robust documentation strategies is therefore one of the most critical steps in audit readiness.

The foundation of documentation is a well-structured policy framework. Organizations should ensure that their data protection policies are current, clearly written, and accessible. These policies must cover data classification, access management, incident response, retention, and disposal. Preparing with documented updates demonstrates that the organization not only establishes but also regularly reviews its controls.

Equally important are records of compliance activities. For instance, training logs, risk assessments, breach reports, and vendor due diligence files all provide concrete evidence of compliance. Maintaining these in a centralized and easily retrievable repository ensures auditors can validate claims efficiently.

Data processing registers form another key area. Internal auditors will expect to see detailed records of what personal data is collected, where it is stored, who has access, and how long it is retained. Preparing such registers in advance not only aids audits but also ensures readiness for regulatory inspections.

Change management documentation is often overlooked but highly relevant. Organizations that implement new systems, migrate to cloud platforms, or alter processes must maintain records of privacy assessments, approval workflows, and testing results. Preparing with these records demonstrates a proactive stance toward risk management.

Incident documentation is also crucial. Even organizations with strong defenses face occasional data breaches or near misses. Preparing with detailed incident reports, root cause analyses, and remediation evidence shows auditors that lessons are learned and improvements applied.

To streamline preparation, organizations should establish standardized templates for documenting compliance activities. This consistency reduces errors, saves time, and ensures uniform quality across departments. Automating document management with compliance software can further reduce the administrative burden while improving accuracy.

Finally, organizations should conduct internal reviews of documentation before the audit begins. Verifying completeness, clarity, and accessibility ensures that evidence supports audit findings effectively. It also prevents delays that could arise from missing or disorganized records.

In conclusion, effective documentation strategies transform audit preparation from a reactive scramble into a proactive process. By maintaining policies, compliance records, processing registers, incident logs, and standardized templates, organizations strengthen their data protection audits and build resilience against regulatory scrutiny.

Documentation lies at the heart of internal audits, particularly in the area of data protection. While strong controls and processes are vital, auditors rely on documentation to validate whether these practices are consistent, effective, and sustainable. Preparing robust documentation strategies is therefore one of the most critical steps in audit readiness.

The foundation of documentation is a well-structured policy framework. Organizations should ensure that their data protection policies are current, clearly written, and accessible. These policies must cover data classification, access management, incident response, retention, and disposal. Preparing with documented updates demonstrates that the organization not only establishes but also regularly reviews its controls.

Equally important are records of compliance activities. For instance, training logs, risk assessments, breach reports, and vendor due diligence files all provide concrete evidence of compliance. Maintaining these in a centralized and easily retrievable repository ensures auditors can validate claims efficiently.

Data processing registers form another key area. Internal auditors will expect to see detailed records of what personal data is collected, where it is stored, who has access, and how long it is retained. Preparing such registers in advance not only aids audits but also ensures readiness for regulatory inspections.

Change management documentation is often overlooked but highly relevant. Organizations that implement new systems, migrate to cloud platforms, or alter processes must maintain records of privacy assessments, approval workflows, and testing results. Preparing with these records demonstrates a proactive stance toward risk management.

Incident documentation is also crucial. Even organizations with strong defenses face occasional data breaches or near misses. Preparing with detailed incident reports, root cause analyses, and remediation evidence shows auditors that lessons are learned and improvements applied.

To streamline preparation, organizations should establish standardized templates for documenting compliance activities. This consistency reduces errors, saves time, and ensures uniform quality across departments. Automating document management with compliance software can further reduce the administrative burden while improving accuracy.

Finally, organizations should conduct internal reviews of documentation before the audit begins. Verifying completeness, clarity, and accessibility ensures that evidence supports audit findings effectively. It also prevents delays that could arise from missing or disorganized records.

In conclusion, effective documentation strategies transform audit preparation from a reactive scramble into a proactive process. By maintaining policies, compliance records, processing registers, incident logs, and standardized templates, organizations strengthen their data protection audits and build resilience against regulatory scrutiny.

About Internal Audit Review

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Newsletter

Subscribe now to get timely updates and in-depth insights designed to keep you ahead of the curve.

© 2026

All Rights Reserved

About Internal Audit Review

A multidisciplinary review board providing independent, forward-thinking guidance alongside leadership to enhance audit quality, anticipate emerging risks, and drive organizational resilience.

Newsletter

Subscribe now to get timely updates and in-depth insights designed to keep you ahead of the curve.

© 2026

All Rights Reserved

About Internal Audit Review

A multidisciplinary review board providing independent, forward-thinking guidance alongside leadership to enhance audit quality, anticipate emerging risks, and drive organizational resilience.

Newsletter

Subscribe now to get timely updates and in-depth insights designed to keep you ahead of the curve.

© 2026

All Rights Reserved