AI Risk Assessment and Control Frameworks – PDF

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BENEFITS OF THIS DOWNLOADABLE PDF DOCUMENT

1. Equips auditors to identify and evaluate the full spectrum of AI-specific risks systematically.
2. Connects identified AI risks to concrete control objectives and testable control activities.
3. Provides an AI risk and control matrix and testing templates ready for immediate audit use.

ARTIFICIAL INTELLIGENCE PDF DESCRIPTION

AI Risk Assessment and Control Frameworks PDF: Download a structured internal audit module aligned with NIST, ISO, IIA standards to evaluate AI risks and controls. AI Risk Assessment and Control Frameworks is a 40-page PDF document available for immediate download upon purchase.

Auditing AI well depends on understanding what can go wrong and what controls should be in place. This is Module 6 of the AI-Powered Internal Audit Professional Series, a 15-module program developed and published by Business Excellence to build practical AI audit capability.
This module gives internal auditors a structured approach to AI risk assessment and to evaluating the control frameworks designed to mitigate AI risk. It works systematically through the categories of AI-specific risk – technical risks such as model performance degradation and data quality failure; operational risks such as inadequate monitoring and unclear ownership; security risks such as unauthorized access to models and training data; compliance risks tied to regulation and policy; and ethical risks including bias affecting protected groups.
For each risk category, the module identifies the control objectives that should be met and the control activities that typically deliver them – for example, bias testing performed during development and at defined monitoring intervals, automated performance monitoring with alert thresholds, and role-based access control with periodic access reviews. Crucially, the module then shows how to design test procedures that evaluate whether those controls are actually operating effectively, and how to document the results.
The centerpiece deliverable is an AI risk and control matrix that auditors populate during planning: each identified risk is linked to existing controls or noted as a control gap, specific test procedures are defined, and test results are recorded during execution to structure the eventual audit report.
The module is aligned with the NIST AI Risk Management Framework, ISO/IEC 42001, the IIA Global Internal Audit Standards 2024, and the EU AI Act, and reflects current guidance from ISACA. It is written in executive-level language appropriate for senior auditors and Chief Audit Executives.
Delivered as a professional PowerPoint presentation with full speaker notes, the module includes the AI risk and control matrix template, control testing templates, risk assessment checklists, real-world examples from cited sources, and a 10-question knowledge assessment quiz – turning AI risk theory into audit-ready practice.

Source: Best Practices in Artificial Intelligence PDF: AI Risk Assessment and Control Frameworks PDF (PDF) Document, Amer Morgan