AI for Continuous Auditing and Monitoring – PDF

VIDEO DEMO

BENEFITS OF THIS DOWNLOADABLE PDF DOCUMENT

1. Shows auditors how to move from periodic testing toward real-time, AI-enabled continuous assurance.
2. Equips teams to design automated controls testing with meaningful alert thresholds.
3. Provides continuous monitoring design templates and implementation checklists ready for use.

AUDIT MANAGEMENT PDF DESCRIPTION

AI for Continuous Auditing and Monitoring PDF: Learn how to implement AI-powered continuous audit and monitoring with templates, checklists, and real-world examples. AI for Continuous Auditing and Monitoring is a 40-page PDF document available for immediate download upon purchase.

Continuous auditing has been an aspiration for decades – AI is what finally makes it practical at scale. This is Module 13 of the AI-Powered Internal Audit Professional Series, a 15-module program developed and published by Business Excellence, and it opens the advanced applications level of the series.
This module shows internal audit functions how to use AI to move from periodic, point-in-time testing toward continuous auditing and continuous monitoring. It explains the distinction between the two, the role each plays in the assurance model, and how AI-enabled automation makes ongoing coverage of high-risk areas achievable without proportional increases in audit hours.
The module addresses the practical design questions that determine whether a continuous monitoring program succeeds: which processes and controls are suitable candidates, what data feeds are required, how automated tests are constructed, and – critically – how alert thresholds are set so that the function receives meaningful exceptions rather than noise. It draws on the automated performance monitoring concepts introduced earlier in the series, where monitoring tools with appropriate alert thresholds are themselves a control that auditors evaluate.
The module also addresses governance of the continuous auditing capability itself: ownership, change management, the integrity of the monitoring logic, and how continuous monitoring results feed the risk assessment and annual planning process rather than sitting in isolation.
Reflecting Deloitte research on audit functions' investment intentions in analytics and AI, the content is aligned with the IIA Global Internal Audit Standards 2024 and the NIST AI Risk Management Framework, and reflects current ISACA guidance. It is written in executive-level language for audit managers and Chief Audit Executives designing the next generation of their assurance model.
Delivered as a professional PowerPoint presentation with full speaker notes, the module includes continuous monitoring design templates, an implementation checklist, a candidate-process assessment framework, real-world examples from cited sources, and a 10-question knowledge assessment quiz – giving audit leaders a practical blueprint for continuous, AI-enabled assurance.

Source: Best Practices in Audit Management PDF: AI for Continuous Auditing and Monitoring PDF (PDF) Document, Amer Morgan