Auditing Artificial Intelligence
Business environments continue to evolve rapidly, and artificial intelligence has become a central driver of that transformation. Organizations now rely on AI to streamline operations, enhance efficiency, reduce costs, and unlock new capabilities across every industry. As AI systems grow more complex—spanning traditional machine‑learning models, generative AI, and MCP‑enabled architectures—the need for strong oversight increases.
Auditors play a critical role in ensuring these systems operate securely, responsibly, and in alignment with organizational goals. This course equips auditors with the skills required to evaluate AI systems end‑to‑end, including understanding AI fundamentals, identifying risks unique to AI, and applying effective audit techniques. Students will learn how to assess AI governance, policies, data management practices, and model behavior, including the evaluation of Large Language Models (LLMs) and agent‑based systems.
Throughout the course, students will participate in hands‑on exercises designed to reinforce practical auditing skills. By developing both conceptual knowledge and practical experience, auditors will be prepared to help their organizations adopt AI safely, ethically, and effectively ensuring that innovation is supported by strong controls, clear accountability, and a well‑governed AI strategy.
CPE:
14
Program Level:
Intermediate
Delivery Method:
Live
Cost :
$675 for this 2 day course
Course Contents/Objectives
I. Introduction
• AI background and terminology
• Machine learning, AI, and GenAI model types
• Data sources and feature engineering
• Deployment strategies – cloud, on‑prem, hybrid
• AI lifecycle and LLMOps framework
• Types of AI systems – LLMs, agents, multimodal, MCP‑enabled systems
• AI bias, adversarial training, and responsible AI principles
II. Understanding AI Prompts/Training
• Building effective prompts
• Common prompt mistakes and failure modes
• Optimizing LLMs and tuning strategies
• System prompts, memory notes, and behavioral conditioning
• How models interpret context, tools, and instructions
III. Managing risk related to AI
• AI vs traditional software risks
• Positive and negative implications of AI adoption
• Risk management techniques for AI systems
• AI standards, policies, and governance requirements
• AI risk registers and control ownership
IV. Challenges for AI Risk Management
• Aligning AI with organizational vision and strategy
• Risk measurement, tolerance, and prioritization
• Harmful bias and fairness concerns
• Unintended or unpredictable model behavior
• Security concerns and expanded attack surface
• Third‑party and vendor AI risks
• Cloud AI service shared‑responsibility models
V. Auditor’s Role in Evaluating AI Efficiency, Bias, and Controls
• Identifying and addressing bias
• Data quality and lineage evaluation
• AI KPIs: efficiency, effectiveness, business impact, technical performance, fairness/compliance
• Harmful bias identification techniques
• Monitoring, auditing, and continuous assurance
• Model cards, system cards, and documentation review
VI. AI Security
• OWASP Top 10 for LLM Applications
• OWASP Top 10 for Generative AI
• OWASP API Security Top 10
• AI vulnerability repositories
• AI policies, committees, and ethical practices
• Data protection and privacy controls
• Validation, training, and environment separation
• Access controls and identity boundaries
• MCP (Model Context Protocol) Security
• Tool‑execution risk
• Scope enforcement
• Delegation and authorization boundaries
• Cross‑tenant isolation
• Logging and replay analysis
VII. AI Auditing and Risk Management Frameworks
• Govern, Map, Measure, Manage – NIST AI RMF
• NIST AI Risk Management Playbook
• Controls for mitigating AI risk
• Well‑Architected Machine Learning Design Principles
• Cloud well‑architected frameworks – AWS, Azure, GCP
• MCP governance and control testing
• Model monitoring, drift detection, and reproducibility controls
Laptop Required
Students are required to have a laptop in order to complete the hands-on exercises. The laptop should meet the following specifications for the student to get the most from the exercises:
• 16 GB RAM or higher
• 25 GB available hard drive space
• Windows 11 professional or later
• Administrator privileges including the capability to install and run tools, as well as disable anti-virus
• VMWare Workstation should be installed
Note: Students will also have the option to follow along through full demonstrations of every lab. Those who prefer to watch rather than perform the hands‑on exercises can do so without missing any content.
Target Audience
• Internal Auditors
• IT Specialist Auditors
• IT Auditors
• IT Audit Managers
• Information System Auditors
• Information Technology Auditors
• Information Security Officers
• Developers
Prerequisites
Participants should be familiar with Internet technologies and commonly used Internet security controls. No other advance preparation is required.
What People Say About Us
We help you see the world differently, discover opportunities you may never have imagined and achieve results that bridge what is with what can be.
Sam
Ahmed
Jerome
Marcus