The Biggest AI Audit Risk Is What You Can’t See

Organizations often view AI risk as a single category. Gartner insights indicate that risk exposure changes significantly depending on how AI is deployed.

When organizations build and operate AI internally, they assume full responsibility for governance, monitoring and outcomes. Risks can include biased or inaccurate outputs, model drift, operational disruption, regulatory consequences and unclear ownership of controls.

Customized vendor AI introduces a different challenge. Organizations may configure models and contribute proprietary data, but visibility into the underlying model remains limited. Gartner notes that risks can include reduced explainability, unexpected changes following vendor updates, compliance challenges and unclear accountability when issues arise.

Publicly available AI tools present yet another risk profile. These tools can expose sensitive information, generate hallucinated outputs, create intellectual property concerns and make governance more difficult because organizations have limited control over usage and system changes.

Because risks vary by deployment type, Gartner recommends that audit teams begin with a clear understanding of the organization’s AI landscape.

An up-to-date AI inventory helps audit teams identify where AI is being used, how it’s deployed and which risks require the most attention. Since inventories are often maintained by second-line functions, internal audit should assess their completeness and reliability and develop supplemental views when necessary.

Gartner also recommends that CAEs participate in AI governance and innovation committees whenever feasible. Participation helps audit teams remain informed about emerging use cases and AI developments while providing a voice in responsible AI adoption discussions.

This visibility helps internal audit focus assurance efforts on the most material risks rather than applying a uniform approach across all AI initiatives.

Once risks are understood, internal audit can select and adapt an AI auditing framework that aligns with organizational priorities, AI maturity and regulatory requirements.

Gartner finds that industry frameworks provide useful foundations but rarely address every assurance need on their own. As a result, audit teams often combine elements from multiple frameworks. Gartner notes that NIST AI RMF can help anchor risk identification and mitigation, ISO/IEC 42001 can support operational controls, ISACA guidance can strengthen audit methodology, COSO ERM can align AI risks with enterprise risk management and COBIT can reinforce IT governance integration.

Rather than adopting a framework as-is, Gartner recommends evaluating where a chosen framework falls short and then adding complementary guidance to address gaps.

Effective AI assurance depends less on the framework organizations choose and more on how well that framework reflects their AI deployments, risks and business objectives.

Audit teams should develop a customized auditing framework based on organizational governance principles and input from subject matter experts. This involves:

• Translating external regulations and standards into clear, tailored audit criteria.
• Integrating audit requirements into existing development workflows to create audit-ready evidence without extra documentation.
• Applying the framework at both enterprise and entity levels, focusing on high-risk topics such as security, privacy and fairness.
• Establishing a recurring audit cycle that rotates across governance, life cycle controls and security posture.

This approach standardizes AI assurance, reduces variability and enables earlier issue identification, supporting responsible AI adoption and risk management.