In a previous article, I discussed how artificial intelligence is increasingly operating as an “unseen employee” across finance, HR, operations, compliance, and other business functions.
These systems are no longer experimental technologies operating on the sidelines. They’re approving transactions, influencing decisions, interacting with ERP systems and automating workflows throughout the enterprise.
In short, they’re given great authority without appropriate oversight.
Like any employee with access to critical systems and decision-making authority, these “employees” introduce risks, which may be amplified with AI operating continuously, at scale, and with limited transparency.
In this rapidly changing risk environment, here are eight key risks finance leaders and internal auditors should evaluate today.
Bias and unfairness
AI systems learn from historical data. If that data contains patterns of bias or inconsistent decision-making, the model may replicate and even amplify those outcomes. Whether AI is screening job candidates, approving expenses or prioritizing vendors, organizations must understand how fairness is measured and monitored.
Lack of explainability
When management cannot explain why an AI system reached a particular conclusion, trust quickly erodes. Boards, regulators, and stakeholders increasingly expect organizations to provide clear, defensible explanations for significant AI-driven decisions. “The model decided” is not an acceptable answer.
Accountability gaps
If an AI-driven decision causes financial loss, compliance violations or reputational damage, who is responsible? Many organizations have yet to establish clear ownership for AI systems. Without defined accountability, errors can go unresolved and control failures can persist unnoticed.
Excessive access and decision rights
One of the most significant control concerns is the authority granted to AI service accounts. In some environments, AI systems can initiate, approve and post transactions within the same workflow, effectively bypassing traditional controls over segregation-of-duties. Internal audit should evaluate whether AI has been granted privileges that would never be acceptable for a human employee.
Integration risks
AI rarely operates in isolation. It often interacts with ERP platforms, payment systems, HR applications, and third-party tools. As integrations expand, so does the potential for control breakdowns, unauthorized activity and data exposure. A single poorly governed AI workflow can create risk across multiple systems simultaneously.
Drift and hallucinations
AI models are not static. Over time, changes in data, business conditions, or user behavior can alter how a model performs. Moreover, generative AI tools may produce inaccurate or entirely fabricated outputs, commonly referred to as hallucinations. Without monitoring and validation, organizations may not recognize these issues until significant damage has occurred.
Regulatory exposure
Regulators are rapidly increasing their focus on robust AI risk management and expect companies to ensure human oversight of high-impact activities and decisions. Organizations that cannot demonstrate appropriate controls, documentation and accountability may face regulatory scrutiny, legal challenges and compliance violations, particularly when AI influences financial, employment or customer-facing decisions.
Over-reliance and deskilling
As AI becomes more capable, employees may become less inclined to challenge it and apply their own institutional knowledge and independent judgment. This presents the risk of employees failing to validate outputs, making companies vulnerable to mistakes that would otherwise be identified through human expertise and skepticism.
Why These Risks Matter
Individually, each of these risks is significant. Together, they represent a fundamental shift in the enterprise risk landscape.
The common thread is authority.
Companies are increasingly empowering AI systems to read data, initiate actions and influence decisions without applying the same governance, oversight and accountability expectations they would impose on human employees performing the same role.
For finance and internal audit, this creates both a challenge and an opportunity. The challenge is identifying where AI is operating and understanding the risks it introduces. The opportunity is helping organizations establish a framework that makes AI trustworthy, transparent and controllable.
A Framework for the AI Age
Fortunately, internal auditors do not have to start from scratch. The Institute of Internal Auditors’ Artificial Intelligence Auditing Framework provides a practical roadmap for evaluating AI governance, management controls and independent assurance activities. The framework aligns AI oversight with familiar audit principles while providing guidance for addressing emerging risks and control requirements.
You can learn more about the framework at: https://www.theiia.org
In an upcoming article, I’ll explore how internal audit functions can apply this framework in practice and take a leadership role in building effective AI governance, oversight and assurance programs.
