Artificial intelligence is no longer a future-state topic for internal audit. It is simultaneously a powerful audit tool and a significant new risk domain that audit must help the organization govern.
AI as an audit enabler. Machine learning and analytics can interrogate entire populations rather than samples, surface anomalies in transaction data, and free auditors from repetitive testing. Used well, AI expands coverage without expanding cost.
AI as a risk to be assured. The same technologies create new exposures — biased or opaque models, data privacy violations, and decisions that cannot be explained to regulators or affected parties. Internal audit should assess:
- Whether AI use cases are inventoried and risk-rated
- The adequacy of model validation and bias testing
- Data lineage, consent and privacy controls
- Human oversight and escalation of AI-driven decisions
Governance before deployment. Organizations adopting AI should establish clear accountability before, not after, deployment. Internal audit's independence makes it well-placed to provide assurance that AI is used ethically, lawfully and within risk appetite.
The functions that thrive will be those that both adopt AI to enhance their own work and provide credible assurance over the organization's AI.