Description

As artificial intelligence becomes embedded in operational and decision-making processes, auditors are expected to provide credible assurance over systems that are complex, opaque, and rapidly evolving.

AI Assurance for Auditors is a methodical, audit-focused guide designed for internal auditors, external auditors, and compliance professionals responsible for assessing AI-enabled systems. The book clarifies what "auditability" means in an AI context and translates governance principles into concrete evidence expectations.

This volume provides structured frameworks auditors can apply across the AI lifecycle, from data sourcing and model development through deployment and monitoring. It emphasizes defensible audit trails, independent testing, and documentation that withstands regulatory and stakeholder scrutiny.

Key areas covered include:

Audit scoping and risk assessment for AI systems Governance controls and accountability structures Evidence requirements for model development and testing Use and evaluation of model cards and documentation artifacts Data lineage, traceability, and change management Validation results, monitoring metrics, and issue management

Written in a precise and practical style, this book equips auditors with repeatable approaches, evidence templates, and review checklists to confidently assess AI governance and controls without requiring deep data science expertise.