AI Risk Management: The Crucial Role of Internal Auditors

As artificial intelligence (AI) continues to advance and becomes more integrated into business operations, internal auditors must stay vigilant about the associated risks.  AI presents exciting opportunities for driving innovation and efficiency,. It also introduces significant challenges that organisations cannot afford to overlook.

Here are six critical AI risks that internal auditors should have on their radar:

  1. Accuracy and Accountability

AI systems can sometimes produce inaccurate outputs or what is termed “hallucinations” – confidently stating incorrect information. This raises concerns about the reliability and accountability of AI solutions, especially in high-stakes domains like finance, healthcare, and legal applications. Internal auditors will need to scrutinise data sources, training processes, and decision-making pathways of AI systems thoroughly to ensure accuracy and traceability.

  1. Ethical Considerations

The development and use of AI carries profound ethical implications. AI algorithms trained on biased data can perpetuate discrimination against certain groups in areas like hiring, lending, and law enforcement. Internal auditors must verify that their organisations prioritise ethical AI practices, including diverse and unbiased training data, rigorous testing, and transparent, explainable models.

  1. Data Privacy Risks

All AI systems require massive amounts of data for training and operation, raising privacy concerns amid tightening data regulations like GDPR and CCPA. Internal auditors must ensure robust data governance frameworks are in place to ensure regulatory compliance  protecting organisations against hefty fines and reputational damage.

  1. Workforce Disruption

While AI can drive efficiency, it also poses risks of workforce displacement across skilled and unskilled roles. Internal auditors should assess their organisation’s human-centric approach, including investments in retraining and reskilling to align the workforce with new AI capabilities.

  1. Intellectual Property and Legal Liabilities

The use of copyrighted data for AI training models raises intellectual property concerns. There are also uncertainties around legal accountability when AI systems make erroneous decisions or damaging actions. Internal auditors will need to ensure that they properly scrutinise IP practices and advocate for clear governance.

  1. Governance and Regulatory Uncertainty

AI’s rapid growth has outpaced the development of standardised regulations and governance frameworks, creating uncertainty that could stifle innovation. Internal auditors should engage with policymakers and stakeholders to shape balanced AI governance balancing innovation and ethical principles.

ZRC have been delivering Internal Audit Transformations to our clients for over 20 years, including unique methodologies to on how to utilise technology with a risk-based, pragmatic approach. If you are considering using AI technologies, talk to us to see how we can support you in achieving your business aims. Please contact us today: or connect with Zeshan Raja on LinkedIn for a free consultation on your Internal Audit challenges, we are here to help!

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