# AI Risk Awareness Training for Compliance and Audit Functions > Source: https://sukruyusufkaya.com/en/training/uyum-ve-denetim-birimleri-icin-yapay-zeka-risk-farkindaligi-egitimi > Updated: 2026-06-15T04:13:08.739Z > Level: all > Topics: AI Risk Farkındalığı, Uyum, İç Denetim, İç Kontrol, Risk Yönetimi, Veri Güvenliği, Denetlenebilirlik, Gölge AI, Üçüncü Taraf Riskleri, İnsan Denetimi, Çıktı Doğrulama, AI Kontrol Çerçevesi, Kullanım Senaryosu Onayı, Kurumsal Politika, AI Yönetişimi **TLDR:** A comprehensive risk-awareness training that helps compliance, internal-control, and audit teams evaluate AI-related data, process, third-party, control, and auditability risks more consciously. ## Açıklama AI Risk Awareness Training for Compliance and Audit Functions is a comprehensive program designed to help professionals working in legal, compliance, internal audit, internal control, risk management, information security, data governance, and related control functions evaluate AI not merely as a new technology that increases speed and productivity, but as a critical risk domain that can create different classes of institutional risk and must be addressed through policy, process, data security, human oversight, and auditability. The training positions AI neither as something to be fully banned nor as something to be freely adopted without limits, but as an institutional responsibility area that must be managed through proper classification, proper control design, and proper oversight. Throughout the program, participants learn systematically what types of risks generative AI and large language models may create within institutions, how AI usage should be assessed from a compliance and audit perspective, which usage scenarios may be considered low, medium, or high risk, how shadow AI usage can be made visible, and how to approach critical issues such as data leakage, misdirection, uncontrolled automation, use of unapproved third-party tools, regulation and policy breaches, lack of logging, traceability gaps, the impact of faulty outputs on business decisions, and risky practices where human oversight is bypassed. The training not only introduces risks, but also shows how those risks surface in real institutional operations, how they should be questioned, and how they can be made more visible. This program addresses a critical institutional need: while AI usage spreads rapidly across business units, compliance and audit teams often lack sufficient visibility into which tool is being used, with which data, for what purpose, and under which level of control. That visibility gap creates not only technology risk, but also data security, regulatory compliance, third-party management, logging discipline, reputation, and internal-control risks. The training reframes AI risk awareness away from abstract concepts and into the language of controls, oversight, and audit. A major differentiator of the program is that it is designed for the real needs of compliance and audit teams. Participants see through examples which control questions should be asked to assess an AI use case, which data and document types create heightened exposure, the difference between open and closed AI tools, why output validation matters, where human approval must remain mandatory, why usage policy and approval mechanisms are critical, which topics should form the starting point of an AI control universe, and how AI topics can be incorporated into future audit planning in a healthier way. As a result, the training builds not only awareness, but also audit-oriented thinking and risk-based assessment capability. By the end of the training, participants gain a practical working model that enables them to define AI-related critical risk areas in their institution more clearly, distinguish acceptable from unacceptable usage patterns more consciously, assess AI risks across data, process, third-party, control, and audit-trail dimensions, develop team-based question sets and control topics, and build a stronger foundation for future AI governance, internal control, and audit activities. ## Kazanımlar - 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.

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.

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