Artificial intelligence adoption is racing ahead. Organizations are accelerating pilots of chatbots and agentic systems to identify opportunities and unlock growth. But while the gas pedal is pressed to the floor, many companies are just starting to think about the seatbelts of strong AI governance.
Companies are running the risk of deploying AI faster than they understand its risks. At the same time, organizations that build trust in their AI systems can accelerate adoption and value more quickly. AI governance frameworks often lag behind the deployment of the technology, leaving organizations vulnerable to accuracy risks, data integrity issues and unintended outcomes.
This is where internal audit may be uniquely positioned to provide guidance to the organization about what those seatbelts could and should look like. The internal audit function can help the enterprise pursue innovation responsibly by encouraging the development of risk management early in the AI lifecycle.
1. Understand the AI landscape
Organizations might have more AI activity than they realize. Beyond formal projects, AI often appears in vendor tools, department-level pilots and early experiments happening outside traditional review channels. Internal audit can help uncover the current state of AI across the organization, including identifying what tools are in use, whether a governance program exists and how pervasive adoption has become.
This broader visibility can help leadership understand where risks may emerge and where governance needs to mature. It can also support more informed prioritization, allowing for high-impact AI systems to receive the appropriate level of scrutiny and control.
2. Assess governance frameworks
Once the landscape is clear, internal auditors can evaluate whether the organization's governance structure is ready for the scale of AI adoption. Governance defines the rules of the road — the policies, principles and decision-making structures that set expectations for responsible AI use.
Internal audit can assess:
- Alignment with leading frameworks (e.g., the National Institute of Standards and Technology AI Risk Management Framework and the International Organization for Standardization 42001), which outline industry-recognized standards for managing AI risk and establishing responsible governance;
- Compliance with evolving local and global regulatory requirements;
- Coverage of important dimensions across the AI lifecycle: transparency, fairness, privacy, security, reliability and accountability;
- Clarity around stakeholder roles and responsibilities;
- Processes for approving, updating and retiring AI systems;
- Whether governance expectations are understood and implemented across teams.
This analysis can identify gaps between policy and practice and help leaders strengthen the foundation needed to support safe, ethical deployment.
3. Test internal control design and effectiveness
Even the strongest governance framework depends on effective controls, which are the guardrails that keep AI systems safe, reliable and aligned with policy. Internal audit can play a critical role in evaluating whether these technical and procedural safeguards are designed appropriately and operating as intended.
This includes testing controls before deployment, when systems are still being designed, and afterward, when models are in production. For example, internal audit can run structured test scenarios against a model, including edge cases and sensitive inputs, to validate that outputs align with expectations and that guardrails are triggered when they should be. They can also review logs to confirm that key decisions and exceptions are properly captured.
These reviews can provide confidence that AI systems are not only compliant on paper but resilient in practice.
4. Continuously engage leadership
AI risk management is an ongoing conversation. Internal audit can help sustain that dialogue by regularly briefing leadership and audit committees on emerging risks, control effectiveness and governance maturity.
Internal audit can elevate accountability at the executive level. AI is reshaping leadership dynamics by requiring executives to look ahead, anticipate emerging risks and make decisions based on future scenarios rather than historical patterns. This proactive engagement reinforces trust, helping AI adoption to align with the organization's values and risk appetite. It also empowers leaders to make informed decisions about where and how to deploy AI next.
From observers to catalysts
AI innovation will continue to evolve faster than regulation or policy. That makes internal audit's involvement more critical than ever. By embedding itself early in the process, internal audit can move from being a back-end reviewer to a front-end catalyst, offering forward-looking insight that helps leaders anticipate what's coming, not just understand what already happened. In this role, internal audit can become an essential partner in shaping how AI is designed, governed and deployed, enabling the organization to innovate with confidence and control.
Ultimately, internal audit's role in AI governance can help support sustainable progress, and innovation acceleration, but not at the expense of trust.
