Internal Auditing in AI-Driven Organizations
Course Overview
- Understand the role and importance of internal auditing in AI-driven organizations.
- Identify key risks and controls specific to AI technologies and data-driven processes.
- Develop an internal audit approach for assessing AI systems and related governance.
- Evaluate compliance with regulatory frameworks and ethical standards in AI usage.
Training Format:In-class, Virtual, In-house
Location:Lagos, Accra, Nairobi, Kigali
Language:English, French
Nigeria Price:
₦330000
Int'l., (Nigeria) Price:
$1000
Ghana Price:
$4000
Kenya Price:
$5500
Rwanda Price:
$6000
Nigeria Price:₦330000
Int'l., (Nigeria) Price:
$1000
Ghana Price:
$4000
Kenya Price:
$4000
Rwanda Price:
$4000
Nigeria Price: ₦330000
Int'l., (Nigeria) Price:
$1000
Ghana Price: $4000
Kenya Price: $4000
Rwanda Price: $4000
Introduction to Internal Auditing in AI-Driven Organizations
- Overview of Internal Auditing
- Principles of internal auditing in modern organizations.
- The evolving role of auditors in AI-driven environments.
- AI in Business Operations
- Understanding AI technologies: Machine Learning, NLP, and Automation.
- How AI is transforming decision-making and business models.
- Key considerations for internal auditing in AI-powered firms.
Risks and Controls in AI Systems
- AI-Specific Risks
- Data privacy and security risks in AI systems.
- Model biases and ethical considerations in AI.
- Operational risks due to automation and machine learning models.
- AI Control Frameworks
- Developing internal controls for AI systems.
- Risk mitigation strategies: Data governance, model validation, and transparency.
Auditing AI Governance and Compliance
- AI Governance Structures
- Roles and responsibilities in AI governance.
- Assessing AI model oversight, accountability, and decision-making.
- Compliance and Legal Frameworks
- Overview of GDPR, AI-specific regulations, and ethical standards.
- Evaluating adherence to AI compliance requirements and best practices.
Conducting AI Audits and Reporting
- AI Audit Methodology
- Planning and executing an AI audit: Key steps.
- Tools and techniques for assessing AI systems and data workflows.
- Audit Reporting and Recommendations
- Structuring audit reports for AI-driven organizations.
- Communicating findings, risks, and recommendations to stakeholders.
- Best practices for continuous monitoring and improvement.
1ST BATCH: Tuesday, February 17, 2026 — Friday, February 20, 2026.
2ND BATCH: Tuesday, June 9, 2026 — Friday, June 12, 2026.
3RD BATCH: Tuesday, October 6, 2026 — Friday, October 9, 2026.
The training methodology integrates lectures, interactive discussions, collaborative group exercises, and
illustrative examples. Participants will acquire a blend of theoretical insights and hands-on practical
experience, emphasizing the application of learned techniques. This approach ensures that attendees return
to their professional environments equipped with both the competence and self-assurance to effectively
implement the acquired skills in their responsibilities.
