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AI for Internal Audit: Continuous Auditing and Control Testing

Using Data and AI to Strengthen Audit Coverage, Detect Exceptions and Support Assurance

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Why Choose Us

The AI for Internal Audit: Continuous Auditing and Control Testing training course enables participants to design continuous auditing activities and apply AI to control testing throughout the audit lifecycle. It addresses data preparation, test design, exception review, reporting and follow-up for a selected process. Participants develop a continuous audit plan and define safeguards that preserve audit quality, confidentiality and professional judgement. This approach supports timely assurance across increasing volumes of transactions, systems and business activity.

Periodic testing and small samples remain valuable, although they may overlook patterns that arise between audit cycles. Examining selected controls and transactions more frequently helps teams identify exceptions earlier and direct audit effort where it is most needed. AI can help auditors analyse large datasets, classify records, review documents, detect unusual activity, and support planning, drafting and summarising audit work. However, unusual transactions are not necessarily control failures, and AI-generated conclusions cannot replace verified audit evidence; auditors must understand data, validate results and clearly record how findings were reached.

This AI for Internal Audit: Continuous Auditing and Control Testing training course will highlight:

  • Designing continuous auditing activities across the audit lifecycle
  • Preparing data for AI-supported control testing
  • Developing tests for selected controls and transactions
  • Using AI to analyse datasets, classify records and review documents
  • Reviewing exceptions and unusual activity with professional judgement
  • Reporting findings, conducting follow-up and defining appropriate safeguards
Why Choose AI for Internal Audit: Continuous Auditing and Control Testing
Course Goals

What are the Goals?

Upon completing this course, participants will be able to:

  • Determine which audit activities and controls are appropriate for continuous testing.
  • Specify data requirements and evaluate the quality of existing records.
  • Create repeatable tests for transactions and critical controls.
  • Apply AI to assist with document review, classification and exception analysis.
  • Differentiate risk indicators from confirmed audit findings.
  • Verify AI-assisted results and preserve an evidence trail.
  • Create dashboards and alerts to enable ongoing audit monitoring.
  • Implement requirements for confidentiality, access control and human review.
  • Produce a phased plan for introducing continuous auditing within an internal audit function.

Who is this Training Course for?

Who Should Attend?

This AI for Internal Audit: Continuous Auditing and Control Testing training course is designed for:

  • Chief Audit Executives
  • Heads of Internal Audit
  • Internal Audit Managers
  • Internal Auditors
  • IT Audit Managers
  • IT Auditors
  • Risk and Assurance Managers
  • Internal Controls Managers
  • Compliance Auditors
  • Audit Data Analysts
Learning Approach

How will this Training Course be Presented?

This course blends instructor-facilitated discussions, audit case studies and data-driven exercises. Participants create control tests, examine sample exceptions and evaluate where AI can provide support without compromising audit evidence. Across the course, they build a continuous audit plan for a chosen business process.

The Course Content

AI and Continuous Auditing Fundamentals

  • The role of continuous auditing within the audit plan
  • Distinguishing continuous auditing from management monitoring
  • Identifying processes and controls suitable for frequent testing
  • Mapping risks, control objectives and available data
  • Understanding AI applications across the audit lifecycle
  • Defining audit ownership, independence and access requirements
  • Assessing data availability and quality
  • Selecting a process for a continuous audit pilot

Designing Data-Driven Control Tests

  • Translating control descriptions into testable rules
  • Identifying required fields, sources and transaction populations
  • Testing completeness, accuracy and consistency of audit data
  • Designing tests for approvals, access, limits and segregation of duties
  • Analysing duplicate, missing or unusual transactions
  • Setting thresholds and determining test frequency
  • Documenting test logic and expected results
  • Reviewing false positives and refining test rules

Applying AI in Audit Analysis

  • Using AI to classify transactions and supporting documents
  • Extracting relevant information from contracts, invoices and records
  • Identifying patterns and anomalies for further investigation
  • Summarising large volumes of audit information
  • Using AI to support risk assessment and audit planning
  • Evaluating model outputs against known examples
  • Recognising inaccurate, incomplete or unsupported AI responses
  • Preserving auditor review and professional judgement

Managing Exceptions, Evidence and Reporting

  • Creating a workflow for reviewing and assigning exceptions
  • Distinguishing data errors, legitimate variations and control failures
  • Obtaining corroborating evidence for potential findings
  • Recording test results, decisions and review history
  • Establishing escalation criteria for significant issues
  • Designing dashboards for trends, exceptions and control performance
  • Communicating findings clearly to control owners
  • Tracking management actions and repeat exceptions

Implementing a Continuous Audit Programme

  • Prioritising controls for a phased implementation
  • Defining responsibilities across audit, IT and process owners
  • Establishing secure access and confidential data handling
  • Managing changes to data sources, systems and test logic
  • Monitoring test effectiveness and alert volumes
  • Evaluating the value and limitations of AI-assisted audit work
  • Presenting a continuous audit pilot plan
  • Developing a 90-day implementation roadmap
Recognition

Certificate

  • • Wallstreet Development Academy Certificate of Completion for delegates who attend and complete the training course.

Frequently Asked Questions

The course focuses on using data and AI to strengthen internal audit activities. It addresses continuous auditing, control testing and the detection of exceptions. It also considers how these capabilities can improve audit coverage and support assurance work.
Continuous auditing is a core topic of the course. The training connects it with the use of data and AI to strengthen coverage, identify exceptions and support assurance. A detailed syllabus explaining the specific continuous auditing methods is not provided in the available course content.
The course focuses on using data and AI to detect exceptions as part of internal audit work. This supports auditors in identifying items that may warrant further review within continuous auditing and control testing activities. Specific analytical models, datasets or AI techniques cannot be verified from the available course information.
The course is directly relevant to audit work involving coverage, control testing, exception detection and assurance. Its workplace focus is the use of data and AI within continuous auditing activities. Further details about practical exercises, case studies or software applications cannot be verified from the available material.
This training is designed to show how data and AI can support broader and more consistent audit coverage. Its focus on continuous auditing enables participants to consider how audit activities can extend beyond isolated reviews. The available course information does not specify particular systems, tools or implementation methods.
The course examines control testing in the context of data and AI. Its stated purpose is to help strengthen audit coverage and detect exceptions that may require attention. The available information does not identify specific control frameworks, tests or technologies used during the training.
The training links continuous auditing, control testing and exception detection with the wider objective of supporting assurance. It presents data and AI as ways to strengthen the coverage available to internal audit. The course page information provided does not define particular assurance standards or reporting approaches.
If you would like further information about this training course, our team is available to provide professional guidance and support: Email: [email protected] In-house training: https://www.wallstreetda.com/in-house-training

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