Is Your Internal Audit Function Keeping Up With AI Risk?

Key Takeaways

• AI Introduces New Risks: AI creates challenges related to privacy, bias, compliance, cybersecurity, and third-party oversight that require increased internal audit attention.
• Traditional Audits Need to Evolve: Conventional internal audit approaches are not designed to assess the dynamic nature of AI systems and their unique risks.
• Continuous Monitoring Matters: Internal audit should provide ongoing oversight of AI governance, data quality, model performance, and security.
• Internal Audit’s Role Is Expanding: Internal auditors must develop AI expertise and help ensure AI is used responsibly, transparently, and compliantly.

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Artificial intelligence (AI) is no longer a future-state consideration. It is embedded in customer service platforms, fraud detection systems, financial forecasting, HR screening, cybersecurity monitoring, and enterprise decision-making processes across industries. As organizations accelerate AI adoption, internal audit functions are being challenged to evolve just as quickly.

The question is no longer whether AI introduces risks to organizations. The question is whether internal audit is equipped to identify, assess, and monitor those risks effectively.

The Growing AI Risk Landscape

AI systems create opportunities for efficiency, automation, and competitive advantage. However, they also introduce a new category of operational, regulatory, ethical, and reputational risks that many traditional audit frameworks were not designed to address.

AI risks cut across business units, making ownership unclear and governance fragmented. Key AI-related risks include:

• Data privacy and security risks stemming from the use of sensitive or proprietary data.
• Algorithmic bias and discrimination that can lead to unfair or noncompliant outcomes.
• Lack of transparency in black-box AI models.
• Regulatory noncompliance as governments rapidly introduce AI governance requirements.
• Model drift and reliability issues that impact decision accuracy over time.
• Third-party AI dependency risks associated with vendors and external platforms.
• Cybersecurity threats, such as prompt injections, adversarial attacks, and AI-generated fraud.

Why Traditional Audit Approaches Fall Short

Traditional audits are designed for stable, rule-based systems. AI systems are adaptive, data-driven, opaque, and continuously evolving, which creates risks that conventional audit methods were never built to evaluate. Internal audit functions that do not adapt to AI risks becoming reactive instead of strategic.

What Modern Internal Audit Functions Should Be Doing

Modern internal audit functions must evolve beyond traditional control testing to effectively address the unique and rapidly changing risks associated with artificial intelligence. Internal audit teams should move from periodic, checklist-based reviews to continuous assurance over how AI systems are designed, governed, monitored, and used throughout the organization. This includes evaluating whether the organization has established clear AI governance frameworks, defined accountability for AI-related decisions, and implemented policies that align with ethical, legal, and regulatory expectations.

In addition, internal auditors should verify that organizations maintain a comprehensive inventory of AI systems and classify them according to their level of risk and impact. Audit activities should extend beyond reviewing technical controls to assessing the quality, accuracy, and integrity of the data used to train and operate AI models, since poor or biased data can lead to inaccurate, discriminatory, or harmful outcomes. Internal audit functions should also review model development and validation processes, including fairness testing, explainability, cybersecurity protections, and ongoing performance monitoring to identify issues such as model drift or unauthorized use.

Furthermore, modern internal audit teams must assess risks associated with third-party AI vendors and generative AI tools, particularly around data privacy, intellectual property, and security vulnerabilities. Appropriate human oversight is also critical, especially for high-impact decisions involving areas such as finance, hiring, healthcare, or compliance. To remain effective in this evolving environment, internal auditors need to build AI literacy, collaborate with data science, cybersecurity, legal, and risk management teams, and adopt more agile and technology-enabled auditing methods.

To Summarize

The role of internal audit is expanding from traditional compliance assurance to broader trust assurance, helping organizations ensure AI is used responsibly, transparently, securely, and in alignment with organizational values and stakeholder expectations. Modern internal audit functions must shift from static compliance reviews to continuous AI assurance focused on governance, transparency, fairness, security, and responsible use of AI.

To learn more about McKonly & Asbury’s Internal Audit services, contact Dave Hammarberg, Partner, or Victor Kong, Senior Manager, who have been providing internal audit services for over twenty years. We would love to discuss how we can assist you with your challenges.

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