• views: 126
• date: 20 May 2026
• posted by: voska89

Complete AAIR certification prep: all 3 domains - ai risk governance and framework integration, and more

What you'll learn

⚡ Understand ai risk governance and framework integration: ai models, frameworks, strategies, and use cases, ai organizational processes and alignment
⚡ Evaluate ai ownership, oversight, and accountability in the context of ai risk governance and framework integration
⚡ Understand ai life cycle risk management: ai design, development/procurement, and documentation, ai model training, testing, and validation
⚡ Evaluate ai implementation, maintenance, and decommissioning in the context of ai life cycle risk management
⚡ Understand ai risk program management, including ai risk scenario identification and assessment (e.g., threats, vulnerabilities, and attacks)
⚡ Evaluate ai controls management (e.g., evaluation, selection, and validation) in the context of ai risk program management
⚡ Practice with exam-style questions designed to mirror the difficulty and format of the AAIR exam
⚡ Prepare for all AAIR exam domains using structured, domain-by-domain study with practice exams

Requirements

❗ No specific prerequisites required. Some familiarity with AAIR-related concepts is helpful but not mandatory — this course teaches everything from the ground up.

Description

This course contains the use of artificial intelligence. However, every lecture recording involves me reading the scripts, and I am fully involved in scripting and production. Be careful buying courses with instructors that don't appear in person. AI courses are becoming quite common on learning platforms.
This course is a complete, structured study program for the ISACA Advanced in AI Risk (AAIR) exam. Built domain by domain against the official exam blueprint, it covers every topic area you need to understand before sitting for the exam. Each lesson is a narrated video that explains how concepts connect to each other and to real-world practice — not just what the definition is, but how a practitioner applies it.
D1 — AI Risk Governance and Framework Integration (37% of the exam) — covers evaluate risk related to ai models/solutions including design, suitability, algorithms, training, drift, and ai life cycle., evaluate ai use cases based on the organization's risk appetite., leverage ai to support the risk management program (e.g., risk profile, reporting, evaluation, risk models, and analysis)., facilitate the integration of ai risk management into an enterprise risk management framework and risk programs., integrate ai risk considerations into existing governance programs., monitor and test organizational processes to identify ai risks., integrate ai-related risk considerations into the change management process., develop and implement an ai risk management framework, including roles and accountability, ai risk policies and procedures, and acceptable risk tolerance levels., assess human oversight controls at critical decision points for risk and ai impact., collaborate with stakeholders to develop and integrate ai risk concepts into enterprise-wide awareness training., assess compliance with applicable ai-related regulations, laws, frameworks, standards, and guidelines., advise on ai-related risk within contracts and service agreements, including data usage and intellectual property., collaborate with stakeholders to address ai trustworthiness and impacts including ethics, bias, privacy, safety, and environmental, social, and governance (esg) implications.. You will understand how each of these areas is tested on the exam and how they connect to real-world practice.
D2 — AI Life Cycle Risk Management (21% of the exam) — covers . You will understand how each of these areas is tested on the exam and how they connect to real-world practice.
D3 — AI Risk Program Management (42% of the exam) — covers conduct risk assessments to identify and classify risks associated with ai., conduct and/or evaluate threat and vulnerability assessments on ai projects/programs., continuously assess and monitor the risk landscape for emerging ai risk., develop and recommend risk treatment strategies for identified ai risks., evaluate controls to manage ai-related risk within the organization's risk tolerance., integrate ai risk considerations into existing risk register and control taxonomies., capture ai risk considerations in enterprise risk metrics and reporting (e.g., board, management, operations)., evaluate ai risk as part of supply chain risk management., collaborate with stakeholders to integrate ai risk scenarios into the enterprise incident management program., incorporate ai-related risk considerations into incident response, bias, the bcp, and drp.. You will understand how each of these areas is tested on the exam and how they connect to real-world practice.
Every domain includes practice questions designed to mirror the style and difficulty of AAIR exam scenarios, covering not just recall but application and analysis. The course closes with full-length practice exams with detailed answer explanations, so you can measure your readiness and focus your remaining study time where it matters most.
Major topics covered: evaluate risk related to ai models/solutions including design, suitability, algorithms, training, drift, and ai life cycle., evaluate ai use cases based on the organization's risk appetite., leverage ai to support the risk management program (e.g., risk profile, reporting, evaluation, risk models, and analysis)., facilitate the integration of ai risk management into an enterprise risk management framework and risk programs., integrate ai risk considerations into existing governance programs., monitor and test organizational processes to identify ai risks., integrate ai-related risk considerations into the change management process., develop and implement an ai risk management framework, including roles and accountability, ai risk policies and procedures, and acceptable risk tolerance levels., assess human oversight controls at critical decision points for risk and ai impact., collaborate with stakeholders to develop and integrate ai risk concepts into enterprise-wide awareness training., assess compliance with applicable ai-related regulations, laws, frameworks, standards, and guidelines., advise on ai-related risk within contracts and service agreements, including data usage and intellectual property., collaborate with stakeholders to address ai trustworthiness and impacts including ethics, bias, privacy, safety, and environmental, social, and governance (esg) implications., conduct risk assessments to identify and classify risks associated with ai., conduct and/or evaluate threat and vulnerability assessments on ai projects/programs., continuously assess and monitor the risk landscape for emerging ai risk., develop and recommend risk treatment strategies for identified ai risks., evaluate controls to manage ai-related risk within the organization's risk tolerance., integrate ai risk considerations into existing risk register and control taxonomies., capture ai risk considerations in enterprise risk metrics and reporting (e.g., board, management, operations)., evaluate ai risk as part of supply chain risk management., collaborate with stakeholders to integrate ai risk scenarios into the enterprise incident management program., incorporate ai-related risk considerations into incident response, bias, the bcp, and drp., AAIR exam prep 2026.

Who this course is for

⭐ Anyone preparing for the AAIR certification exam.

Homepage

https://www.udemy.com/course/aair-cert

   
   
   

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