AI Risk Awareness Training for Compliance and Audit Functions

Detailed Content (EN)

This training is designed to help compliance and audit units evaluate AI not merely as a topic for technology teams, but as a direct matter of institutional risk, control, accountability, and auditability. The core objective of the program is to make AI-related risks more visible within the institution, make those risks discussable not only at a technical level but also at a managerial and operational level, and help compliance and audit functions become more prepared for this new domain.

Throughout the training, participants learn the main risk types arising from institutional use of generative AI and large language models, how data security intersects with AI usage, why human oversight is critical, which use cases require stronger oversight, and how AI risk can be integrated into internal control, policy, process, and audit frameworks. Concrete topics include unapproved tool usage, entering sensitive data into prompts, using model outputs without validation, insufficient scrutiny of third-party providers, the spread of AI usage without institutional logging discipline, and gaps between policy and operations.

A major focus of the program is the daily reality of compliance and audit teams. Many employees may use external AI tools to gain speed; however, which of those patterns are risky, which data types should never be shared, in which workflows human approval must remain mandatory, and which outputs should never be treated as final truth are often unclear. The training clarifies these uncertainty areas and provides compliance and audit teams with a practical framework for questioning AI risk.

The program also does not leave AI risk awareness at the level of theory. Participants see through examples which questions should be asked from the perspective of an auditor or compliance professional, where control gaps may emerge, which usage examples should be logged, which risk categories must be surfaced when working with third-party platforms, and how risk-based use classification improves institutional decision quality. As a result, the training builds not only awareness, but also an institutional assessment reflex.

By the end of the program, participants can see core AI risk maps more clearly, distinguish acceptable from unacceptable usage patterns more effectively, develop team-based question sets and control topics, integrate AI risk more strongly into audit planning, and build a more conscious readiness foundation for safe, measured, and traceable AI usage. In this sense, the training is not only an awareness program, but a practical institutional-readiness program that strengthens the role of compliance and audit functions in the age of AI.

Who Is This For?

• Compliance, internal audit, internal control, and risk-management teams
• Information-security, data-governance, and policy teams
• Professionals working in legal and institutional-control functions
• Process owners and business-unit managers in highly regulated institutions
• Digital transformation, AI project, and governance teams
• Organizations seeking to make AI usage more controlled, secure, and auditable

Highlights (Methodology)

• Use cases adapted to the real decision and control flows of compliance and audit teams
• A holistic structure combining risk awareness, data security, control design, and audit perspective
• Live examples, case discussions, and application flows focused on developing question sets
• An approach centered on the balance between productivity, control, auditability, human oversight, and data security
• Content focused on third-party tools, shadow AI, output validation, and approval mechanisms
• Reusable control topics and risk-prioritization frameworks for teams

Learning Gains

• Define more clearly the critical risk areas created by AI usage
• Distinguish more consciously between acceptable and unacceptable usage patterns
• Assess AI use cases across data, process, third-party, and control dimensions
• Identify areas that require human oversight, approval mechanisms, and output validation
• Develop team-based question sets, control topics, and evaluation frameworks
• Create a stronger institutional-readiness foundation for future AI governance and audit activities

Frequently Asked Questions

• Does this training require technical knowledge? No. The training focuses not on technical model building, but on increasing AI risk awareness and assessment maturity among compliance and audit teams.
• Is this training only for internal-audit teams? No. It is also suitable for compliance, internal control, risk, information security, legal, data governance, and relevant business-unit managers.
• Can it be customized for institution-specific processes and regulations? Yes. The content can be tailored based on the institution’s sector, regulatory intensity, data sensitivity, third-party structure, and existing control maturity.
• Does this training produce concrete outputs? Yes. By the end of the program, the institution will have a clearer framework around core risk areas, control questions, high-caution use cases, and safe-usage awareness.