GUEST BLOG POST
Artificial Intelligence is no longer a futuristic concept. It’s here, embedded in everything from recruitment software and customer service chatbots to financial forecasting tools and fraud detection systems. But with this unprecedented power comes unprecedented risk. From biased decision-making to data-privacy violations, AI systems are capable of harm—sometimes without anyone even realizing it.
That’s where internal audit can step in as a trusted advisor helping organizations build AI systems that are lawful, ethical, and transparent from the ground up.
The Shift from Auditing Systems to Auditing Intelligence
Traditional internal audits have focused on verifying compliance with laws, policies, and controls. But AI changes the equation. Unlike conventional systems that follow static rules, AI systems learn, adapt, and evolve based on data, often without human intervention. This makes them difficult to explain, predict, or even understand.
Internal auditors now face a dual challenge: Evaluating how AI models are built and trained (including data quality and fairness); and assessing the ethical and legal implications of AI output decisions over time.
See Also, “Survey: Internal Audit Use of Artificial Intelligence Growing Rapidly“
To do this effectively, internal auditors must broaden their scope and skill sets—moving beyond IT controls to also understand algorithmic logic, data governance, and responsible AI principles.
What Needs to Change in the Internal Auditor’s Evolving Toolkit
To meaningfully evaluate AI systems, internal audit teams must evolve their approaches:
1. Integrate AI Auditability into the Internal Audit Plan
Auditability must be baked into AI systems from the start. This includes maintaining logs of training data, version control for algorithms, and documentation of decision logic.
2. Adopt an Interdisciplinary Internal Audit Model
Effective AI audits require collaboration. A typical AI audit team should include not just auditors, but also:
- Data scientists
- Legal and ethics officers
- Domain experts
- Software engineers
- Social scientists
Together, they ensure the audit captures technical, ethical, and societal dimensions.
3. Embedding AI Risk into the Operational Audit Universe
AI shouldn’t be treated as an isolated IT asset. Instead, auditors should map AI risks directly to core operational areas:
- Production line automation
- Quality assurance algorithms
- Safety monitoring systems
- Environmental compliance tools
- Predictive maintenance programs
Audit planning must incorporate AI risks into these process-level reviews, assessing both technical and ethical implications.
4. Auditing the “Black Box” with a Process Perspective
Many industrial AI tools—like vision-based defect detection or dynamic scheduling engines—operate with limited explainability. Internal auditors must:
- Review the design inputs and data sources used to train AI.
- Validate whether human oversight is built into critical decision loops.
- Ensure there is traceability—can you understand why an AI flagged a product as defective or skipped a maintenance check?
5. Promote Explainability and Transparency
Auditors should push for the use of interpretable models or tools that provide explanations of AI outputs in business-relevant terms. Systems that act like “black boxes” must be redesigned to allow traceability and accountability.
6. Develop AI-Specific Checklists and Frameworks
Emerging frameworks like ISACA’s AI Audit Certification and European Commission’s AI Act are providing the building blocks for standardized assessments. Internal auditors should adopt and adapt these as needed.
What Internal Audit Teams Can Do Now
While AI regulations are still catching up, organizations don’t have to wait to act. There are proactive steps internal audit teams can take right now to build readiness and oversight around AI in operational environments.
Start by training internal auditors on core AI principles, including how algorithms function, the basics of machine learning, and the importance of data ethics. A foundational understanding will allow auditors to ask the right questions and better assess AI risks in production or compliance processes.
Next, it’s important to update internal audit charters to reflect AI’s growing role in operations. By clearly defining AI-related responsibilities—such as reviewing data sources, model behavior, or decision transparency—auditors can approach AI with the same rigor they apply to financial or safety controls.
Organizations should also engage early with AI development or implementation teams. When auditors are involved from the design phase, they can help ensure systems are built with auditability in mind—whether that means ensuring access to logs, enabling human overrides, or documenting training data sources.
Another critical step is to demand transparency from external AI vendors and service providers. Whether it’s a quality inspection tool or a predictive maintenance system, auditors must have access to understand how the AI makes decisions, what data it relies on, and how often it is retrained or monitored.
Finally, stay connected with the broader ecosystem. Collaborate with regulators, industry groups, and peer organizations to keep pace with evolving AI best practices, assurance frameworks, and audit tools that are emerging across industrial sectors.
AI will only continue to grow in influence, shaping decisions that impact lives, businesses, and society at large. Internal audit must rise to the occasion, before AI decisions become too complex to question, and too integrated to fix. 
Prasant Prusty is the Founder and CEO of Smart Food Safe, with a wealth of expertise in managing, improving, and critically evaluating food safety and quality processes to globally recognized standards in various food industry segments across the global food supply chain.
Arundhathy Shabu is a Food Technologist, currently working with Smart Food Safe as the Technical Content Specialist. Smart Food Safe is a leading provider of software solutions for end-to-end Quality, Food Safety, Regulatory, and Traceability management.