The Importance of AI Governance and Internal Controls for Businesses
08 July 2025
Written by: Jeff Ritchie, CPAWhat moves you forward could also set you back. Is your business built to withstand the risk?
Artificial Intelligence (AI) continues to become more ingrained in our everyday lives. From helping us craft the perfect email, assisting businesses with code development, or offering recommendations for things to do in a new location, AI is rapidly impacting both our personal and professional worlds.
While AI presents a number of new benefits for businesses, its use also introduces new risks. When organizations adopt AI in their operations, from customer service chatbots to predictive analytics and process automation, they need to understand how essential it is to have a robust AI governance model and strong internal controls in place.
Benefits of Artificial Intelligence in Business
Businesses in every industry are embracing AI to improve operational efficiency, reduce costs, increase revenue, enhance customer experiences, and gain insights to make better decisions. AI is the latest tool organizations are using to remain competitive in a rapidly changing world.
Inherent Risks of Artificial Intelligence
Many organizations have adopted AI in some capacity but may not fully understand the inherent risks. Here are a few that should be considered:
- Data Privacy and Security: If AI is being used by your organization, how are you ensuring that no personally identifiable information (PII) or protected health information (PHI) is being input into the tool? Unless you have an enterprise version of ChatGPT, Microsoft Copilot, or Anthropic, data input often goes into the public domain—potentially putting your organization at risk of disclosing sensitive information. Intellectual property, trade secrets, and financial data may also be at risk when using certain models.
- Hallucinations: AI is not infallible. It does a great job providing answers, but that doesn’t mean those answers are accurate. Relying on inaccurate output can result in poor business decisions, operational inefficiencies, financial losses, or regulatory fines.
- Bias: AI is trained on large datasets, and AI bias refers to biased results a model may produce. Organizations need to be aware of this potential when adopting AI.
- Lack of Explainability and Transparency: AI models often operate in a “black box,” where users cannot understand how the model reached a conclusion. Transparency allows users to better grasp how the system was built and how it generates results.
Failing to understand the risks of AI can lead to operational, reputational, financial, and regulatory consequences.
AI Governance and Internal Controls
The first step in reducing AI-related risk is understanding where in your organization it’s currently being used and for what purpose. Once that’s clear, organizations should establish an AI governance model to oversee and monitor its use and prevent the risks outlined above.
AI governance typically includes developing AI-specific policies and procedures, formally assigning oversight responsibilities, establishing a reporting structure, and implementing internal controls. Depending on how AI is being used, relevant controls might include data validation, algorithm and model testing, performance audits, and human-in-the-loop (HITL) oversight.
Moving Forward
As organizations evolve and adopt AI, they must also adopt a mature governance model and internal controls to unlock its full benefits while minimizing risk. AI can help businesses outpace competitors, but without proper oversight, it can also lead to preventable failures. Organizations that understand this will not only protect themselves, customers, and stakeholders, but also position themselves to fully realize the promise of AI.
MEET OUR EXPERT:
Jeffrey R. Ritchie, CPA
Principal, Risk and IT Assurance jritchie@fustcharles.com
Contact Jeff to discuss how we can help your organization build proactive risk strategies that support innovation and long-term resilience.
Jeff Ritchie is a risk and controls specialist with experience helping public and private organizations assess and manage their risk landscapes. With expertise in both operational and IT control environments, Jeff delivers tailored insights that support smarter decision-making and streamlined risk management. He has extensive experience in System and Organization Controls reporting (SOC 1 and SOC 2), enterprise risk management, internal control transformation, SOX 404 compliance, IT control assessments, process improvement, and control readiness assessments.
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