Auditing Artificial Intelligence: A Handbook for Audit, Risk,...

Description

Auditing Artificial Intelligence: A Handbook for Audit, Risk, and Security Professionals.

Master the principles of auditing, governing, and securing Artificial Intelligence systems in today’s rapidly evolving digital landscape.

As Artificial Intelligence continues to reshape businesses across every industry, organizations face growing challenges related to governance, security, compliance, ethics, and risk management. Auditing Artificial Intelligence provides a practical roadmap for IT auditors, cybersecurity professionals, compliance specialists, and risk managers responsible for evaluating AI systems and ensuring they operate safely, responsibly, and effectively.

This comprehensive guide presents a structured approach to auditing AI throughout its lifecycle, helping readers assess governance frameworks, regulatory compliance, model transparency, ethical considerations, operational performance, and security controls. Combining established industry standards with practical audit techniques, the book equips professionals to identify potential risks and strengthen organizational oversight of AI technologies.

Across 24 in-depth chapters, you’ll explore the key principles and best practices for evaluating modern AI deployments, including:

Key Topics Covered

• AI Governance and Ethical Oversight – Develop governance frameworks that promote accountability, transparency, fairness, and responsible AI adoption.
• Risk Management and Regulatory Compliance – Understand evolving legal requirements and industry standards, including GDPR, the EU AI Act, ISO frameworks, and other compliance considerations.
• Bias Detection and Trustworthy AI – Evaluate machine learning models for bias, fairness, reliability, and consistency in automated decision-making.
• AI Security and Continuous Monitoring – Protect AI systems against adversarial attacks, model manipulation, and operational risks through ongoing monitoring and security controls.
• Model Performance and Explainability – Assess prediction accuracy, interpretability, and alignment with organizational goals while improving model quality over time.

Designed specifically for professionals responsible for evaluating AI environments, this book blends practical methodologies, real-world audit scenarios, industry best practices, and actionable assessment techniques. Readers will gain the confidence to audit AI governance programs, identify emerging risks, verify regulatory compliance, and support the development of secure, ethical, and resilient AI systems.

Whether you’re conducting AI audits, strengthening governance frameworks, managing compliance initiatives, or overseeing enterprise AI risk, this handbook provides the knowledge and tools needed to navigate the complexities of modern artificial intelligence with confidence.

Stay ahead of rapidly evolving AI technologies by learning how to evaluate their deployment, monitor their impact, and help ensure they remain secure, transparent, compliant, and ethically aligned with organizational objectives.