The future of internal audit: AI risk at the speed of trust

As AI moves into hyperdrive, internal audit needs to revamp its approach to detecting and managing risk.

In brief

• By acting as Risk Strategists, internal audit teams can help organizations stay ahead of the growing risks presented by AI.
• While most organizations expect AI to transform risk management, adoption so far has focused largely on incremental improvements in compliance and reporting.
• To drive sustainable success and deliver the greatest value from AI, organizations need to redesign internal auditing processes, not automate existing ones.

Within the internal audit function, AI risk management is proving to be something of a double-edged sword: while a powerful tool for combating risk at speed and scale, it has also become a source of rapidly evolving risk on its own. The future that internal audit and risk leaders have been preparing for is no longer on the horizon. It is already here. As AI becomes embedded across the enterprise, traditional approaches to assurance are being challenged by risks that are now more dynamic, compressed, and difficult to predict and prioritize.

Why AI is changing internal audit’s role

The majority of organizations are concerned that the pace of AI innovation is beginning to outpace traditional approaches to risk management and assurance. According to the Risk Transformation research, 73% of organizations believe they are not fully prepared to manage risk in a NAVI environment.

But to provide foresight into potential issues like cyber attacks, data reliability issues and regulatory risk, internal audit must adapt in lockstep with innovation, developing new procedures that enable more timely risk sensing and response.

This is where the theoretical “rubber meets the road” for many organizations. Can management demonstrate appropriate oversight of AI-enabled decisions? Are controls working as intended? Are accountability mechanisms clear? These questions are becoming increasingly important to boards, audit committees and regulators alike.

Historically, annual or cyclical audit planning was better suited to risks that developed at a pace that allowed organizations to assess, test and report on issues after the fact. But today’s risks can evolve significantly over the course of an audit itself. By the time findings reach the audit committee, the underlying technology, exposure or operating environment may already have changed.

Internal audit leaders must now develop audit plans for risks that move faster, connect across domains and could potentially disrupt the entire organization before traditional review cycles can respond. The core challenge is not simply whether risk teams receive information quickly enough, but also whether they have sufficient evidence, transparency and accountability to act on AI-enabled insights while there is still time to influence the outcome.

Internal audit must help leaders recognize relevant signals and understand what those signals mean for the organization. In this way, AI becomes both a tool for managing AI-enabled risk and a tool that supports decision speed. Without that foundation, faster technology can still produce slower decisions.

Adding continuous monitoring that considers both external and internal outputs will help provide additional assurance for management teams. Internal audit cannot operate in a silo; it must work closely with risk, compliance, privacy and security teams to develop a connected risk approach that reviews the entire organization for model, cyber, regulatory and operational risks.

Why “bolt-on AI” won’t suffice

The greatest value internal audit can realize from AI comes from redesigning processes rather than automating existing ones. Using AI to summarize information, document walk-throughs, organize evidence and draft reports may seem like low-hanging fruit to prioritize, but while these are worthwhile improvements on their own, they capture only a fraction of AI’s broader potential.

As organizations adopt AI, many are choosing a “bolt-on” approach, which is characterized by isolated use cases and disconnected pilots. It is not as sustainable as a built-in approach, which reimagines how the work is fundamentally executed, setting the stage for new business models, experiences and long-term value creation.

To keep pace with risk, internal audit will need to redesign processes to amplify how the function senses risk, tests control accuracy, adapts coverage and provides assurance in an AI-enabled risk environment. The distinction is not whether internal audit uses or doesn’t use AI. It is between viewing AI as a point solution that improves individual activities and treating the technology as a strategic capability that fundamentally redesigns how assurance is delivered.

Built-in AI supports a stronger foundation for governance, trust and resilience. Embedding AI, security, controls and decision-making directly into workflows makes scaling AI more sustainable and helps organizations adapt as business conditions change.

The following table summarizes the differences between the bolt-on and built-in approaches:

Sense, test, connect and adapt: A practical framework for using AI in internal audit

At its best, AI should instead allow internal audit to focus on the right risks, at the right time. Combined with analytics and continuous monitoring, earlier intervention and more informed responses is where investment in AI proves its value. Rather than replacing professional judgment, this approach instead directs that judgment toward the issues that require attention most urgently.

Here’s a deeper dive into those points.

1. In a rapidly changing environment, the ability to sense changing risks before they become a crisis will be critical. To that end, organizations should:
   • Monitor issue management systems to identify recurring control failures.
   • Identify emerging themes across audit reports and management actions.
   • Track regulatory developments and map potential impacts to the audit universe.
   • Observe third-party or cyber incident activity that could require audit attention.
   • Deploy AI-enabled risk sensing to identify weak signals, emerging patterns and changes in exposure between audit cycles.
2. As AI scales, the necessary controls and resilience capabilities will need to scale at the same time. That means:
   • Testing entire transaction populations instead of samples.
   • Identifying any unusual patterns, exceptions or outliers requiring auditor review and escalating matters that require human judgment, interpretation or challenge.
   • Reviewing user access activity and segregation-of-duties conflicts.
   • Assessing governance and monitoring controls supporting AI-enabled processes.
3. In the design of AI-enabled controls and processes, audit teams need to take steps to thoroughly mirror the interconnected nature of risk, connecting risks, controls and assurance insights and dependencies across the enterprise. Key actions include:
   • Aligning audit findings with enterprise risks and control owners.
   • Identifying recurring issues appearing across multiple business units.
   • Mapping dependencies between cyber, operational, privacy and third-party risks.
   • Providing leadership with a connected view of resilience exposures.
4. In a NAVI world, the only constant is change. Assurance needs to adapt accordingly, which means internal audit should be ready to:
   • Initiate reassessment of coverage following significant incidents.
   • Update risk assessments when new AI initiatives are deployed.
   • Adjust audit plans in response to regulatory change.
   • Escalate emerging risks to management and the audit committee.

Conclusion

In a NAVI environment, resilience increasingly depends on the speed, scale and insight AI can provide. However, organizations cannot fully realize those benefits without trust. That’s where internal audit is uniquely positioned to step up to the challenge by evolving from periodic reviews to continuous learning, risk sensing and adaptation, while fundamentally redesigning how assurance is delivered. In doing so, internal audit builds and sustains the trust that enables organizations to respond to changing risks, make decisions with confidence and realize the full value of AI.