About the Course

Internal audit functions face a dual challenge: they need to use AI to improve audit efficiency while also providing assurance over AI-related risks within the organization. Generative AI can accelerate planning, documentation review, control analysis and reporting, while analytics can strengthen anomaly detection and risk prioritization. At the same time, auditors must assess governance, data, model, third-party and operational risks created by AI systems.

This course develops both dimensions of capability. Participants learn how to apply AI across risk assessment, audit planning, walkthrough preparation, document analysis, control testing support, data analysis, issue formulation and report drafting. They also examine how to scope audits of AI governance, model oversight, generative AI use, vendor controls and management processes, with reference to recognized AI risk and management frameworks.

Through audit simulations and case exercises, participants develop an AI audit-use protocol, AI-enabled audit workpaper templates, an AI risk universe, an AI governance audit programme and assurance reporting tools. The course emphasizes evidence, professional skepticism, confidentiality and human review so that AI increases audit capability without weakening audit quality.

Target Participants

This course is intended for chief audit executives, internal auditors, IT auditors, risk and compliance professionals, assurance specialists, audit managers, control functions, governance professionals and auditors responsible for assessing digital, technology or AI-related risks.

What You Will Learn

By the end of this course the participants will be able to:

• Apply AI tools to selected stages of the internal audit lifecycle.
• Use AI to support audit planning, document review, control analysis and reporting.
• Evaluate the reliability and limitations of AI-generated audit support.
• Develop an AI risk universe for audit planning.
• Scope assurance work over AI governance, generative AI and third-party AI use.
• Design audit procedures for AI-related governance, risk and control areas.
• Apply confidentiality, documentation and professional-skepticism safeguards.
• Develop an internal-audit AI adoption and assurance plan.

Course Duration

• Classroom – 5 Days
• Online – 7 Days

Course Outline

AI Audit Foundations

• AI opportunities in internal audit
• Audit risks from AI use
• Professional skepticism in AI-assisted work
• Confidentiality considerations
• Human accountability for audit conclusions

Risk Assessment

• AI-assisted risk identification
• Risk-universe refinement
• Emerging-risk scanning
• Audit prioritization prompts
• Validating AI-generated risk hypotheses

Audit Planning

• Engagement objective drafting
• Scope refinement
• Audit-programme generation
• Interview-question preparation
• Planning-memo support

Document Review

• Policy and procedure analysis
• Contract and control-document review
• Exception extraction
• Cross-document comparison
• Evidence traceability

Control Analysis

• Control design assessment
• Control-gap identification
• Risk-control mapping
• Segregation-of-duties review support
• Control test procedure drafting

Audit Analytics

• Data anomaly concepts
• Trend and ratio analysis
• Exception identification
• Sampling support
• Interpreting analytics with professional judgment

Issue Development

• Condition-criteria-cause-effect structure
• Root-cause analysis support
• Risk-rating consistency
• Recommendation quality review
• Management-response analysis

Audit Reporting

• Executive-summary drafting
• Finding clarity and concision
• Report consistency checks
• Tone and stakeholder adaptation
• Human review before issuance

AI Assurance

• AI governance audit scope
• AI inventory and accountability
• Generative AI control review
• Third-party AI assurance
• AI monitoring and incident controls

Audit AI Framework

• AI use protocol for auditors
• Approved tools and restricted data
• Workpaper documentation requirements
• Quality-assurance checkpoints
• Internal audit AI roadmap

Practical Outputs

• Internal-audit AI use protocol
• AI-enabled audit planning prompt pack
• AI risk universe
• AI governance audit programme
• AI-assisted finding template
• AI assurance checklist
• Internal audit adoption roadmap

Training Approach

Participants work through audit scenarios, risk-assessment exercises, control reviews, document-analysis labs and report-writing simulations. Practical sessions require participants to test AI outputs against audit evidence and professional standards. A parallel assurance track develops the ability to audit organizational AI governance and use rather than focusing only on auditor productivity.

Organizational Benefits

• More efficient audit planning and documentation analysis
• Improved audit coverage of emerging AI risks
• Stronger consistency in issue formulation and reporting
• Better assurance over AI governance and third-party use
• Clearer controls for auditors’ own use of generative AI
• Enhanced audit capability without reducing professional skepticism

Certification

Participants who successfully complete this course will receive a Certificate of Course Completion from Devimpact Institute, recognizing the knowledge, practical skills and competencies developed through the programme.

Tailor-Made Course

This course can be customized and delivered as an in-house programme to address the specific policies, systems, sector context and capacity-development priorities of an organization. The course duration, modules, case studies and practical exercises may be adjusted following a training needs assessment.