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Full Description
Artificial Intelligence (AI) is revolutionizing industries, yet its rapid evolution presents unprecedented challenges in governance, ethics, and security. Auditing Artificial Intelligence is an essential guide for IT auditors, information security experts, and risk management professionals seeking to understand, evaluate, and mitigate AI‑related risks.
This book provides a structured framework for auditing AI systems, covering critical areas such as governance, compliance, algorithm transparency, ethical accountability, and system performance. With 24 insightful chapters, it explores topics including:
AI Governance and Ethics - Establishing frameworks to ensure fairness, accountability, and transparency in AI deployments.
Risk Management and Compliance - Addressing the legal and regulatory landscape, including GDPR, the EU AI Act, and ISO standards.
Bias and Trustworthiness - Evaluating AI decision‑making to detect bias and ensure equitable outcomes.
Security and Continuous Monitoring - Safeguarding AI systems from adversarial attacks and ensuring operational consistency.
Model Performance and Explainability - Assessing AI outputs, refining accuracy, and ensuring alignment with business objectives.
Designed for professionals tasked with assessing AI systems, this book combines practical methodologies, industry standards, and real‑world audit questions to help organizations build responsible and resilient AI practices and assess associated risks. Whether you are assessing AI governance, monitoring AI‑driven risks, or ensuring compliance with emerging regulations, this handbook provides the guidance you need to navigate and assess the complexities of AI systems with confidence.
Stay ahead in your role and responsibility for assessing the rapidly evolving deployment and use of AI across the organization - equip yourself with the knowledge and tools to ensure its responsible, safe, approved, secure, and ethical use.
Contents
Part 1 AUDITING ARTIFICIAL INTELLIGENCE
Chapter 1 Artificial Intelligence: The Basics
Chapter 2 AI Algorithms
Chapter 3: Key Metrics for Generative AI
Chapter 4 AI KPIs
Part 2 AI GOVERNANCE AND OVERSIGHT
Chapter 5 AI Governance and Oversight
Chapter 6 Auditing AI Accountability
Chapter 7 Auditing The Trustworthiness of AI
Chapter 8 AI Systems Decision-Making: Auditing Fairness
Chapter 9 Auditing The Reliability of GEN-AI Tools
Chapter 10 Auditing the Ethics of AI
Chapter 11 Auditing the Process of Prompt Engineering
Part 3 AI RISK MANAGEMENT
Chapter 12 AI Risk Management
Chapter 13 Auditing AI Bias and Deception Risk
Chapter 14 Risk Assessment: Auditing AI Software vs Traditional Software
Part 4 AI REGULATORY AND COMPLIANCE
Chapter 15 Auditing The Status Of AI Compliance
Part 5 AUDITING AI SYSTEMS
Chapter 16 Auditing AI System Documentation and Reporting
Chapter 17 Reinforcement Learning Models and Reward Reports
Chapter 18 Code Analysis
Chapter 19 Change Management and AI Systems
Chapter 20 Safety and Security of AI Systems and Software
Chapter 21 Software Certification of AI Systems
Chapter 22 Auditing AI System Performance
Chapter 23 Auditing AI Systems: Continuous Monitoring
Chapter 24 Data Collection and Operational Consistency
