AI Applications and Challenges for Auditors Course

Module 1: Introduction to AI – History, Data Influences, and Technical Progression

• Define Artificial Intelligence and explore its historical development
• Understand the role of structured and unstructured data in AI
• Analyze how data sources like the Surface Web, Deep Web, and Dark Web feed AI systems
• Discuss cloud infrastructure, big data, and the exponential growth of information

Module 2: LLMs, SLMs, and Artificial Intelligence Architecture

• Differentiate between Large Language Models (LLMs) and Small Language Models (SLMs)
• Explore the structure of AI: AI, Machine Learning, and Deep Learning
• Review major AI tools and platforms (ChatGPT, Bard, Claude, Gemini, LLaMA, Copilot)
• Identify AI applications in transportation, healthcare, fraud detection, and creative work

Module 3: AI Products and Searching Techniques

• Compare AI chat interfaces vs. traditional search engines for audit research
• Understand data privacy concerns including PII and PHI exposure in AI usage
• Conduct AI-powered anomaly detection using real audit datasets
• Create audit summaries using AI and assess their risk implications

Module 4: AI Cautions and Ethical Considerations

• Explore ethical challenges such as hallucinations, bias, propaganda, and data misuse
• Examine AI governance frameworks: EO 13859, 13960, 14110, 14141, 14179
• Understand national and international efforts (NIST, UK, EU) for responsible AI use
• Review the NIST AI Risk Management Framework (AI RMF 1.0)

Module 5: Use Cases in AI

• Identify AI applications in document summarization and audit automation
• Explore additional use cases: P-card analysis, geo-mapping, fuzzy matching, vendor data
• Detect AI-generated content in text and images using forensic and analytic techniques
• Discuss the future of AI: AGI, ASI, rogue models, monetization, and workforce impacts