Governance, Risk & Compliance
Artificial Intelligence (AI) Governance, Risk and Compliance
This five-day programme prepares professionals to govern AI across its full lifecycle, covering AI fundamentals and lifecycle, AI governance structures and frameworks, AI risk assessment and regulatory compliance, AI controls, testing and human oversight, and monitoring, assurance and reporting. Participants develop skills in AI governance structuring, AI risk assessment, AI control and testing design, and AI assurance reporting.
Introduction
Artificial Intelligence (AI) Governance, Risk and Compliance Training Course prepares professionals to govern AI across its full lifecycle. Participants work through AI fundamentals and lifecycle stages, AI governance structures and accountability, the NIST AI RMF and ISO/IEC 42001, AI risk assessment across model, data, and ethical risk, AI regulatory compliance including the EU AI Act and data protection law, AI controls and testing, and AI monitoring, assurance, and reporting as one connected discipline. The programme links the AI systems an organisation deploys with the governance, risk, and compliance discipline that keeps them accountable. As a result, learners gain a practical command of AI GRC. Furthermore, the course strengthens AI risk judgement, control design, and compliance literacy.
Artificial Intelligence (AI) Governance, Risk and Compliance Course Objectives
- Explain AI concepts, capabilities, and lifecycle stages.
- Apply AI governance structures, policies, and accountability models.
- Apply the NIST AI RMF and ISO/IEC 42001 requirements.
- Apply the EU AI Act risk classification and obligations.
- Apply AI risk assessment across model, data, ethical, and legal risk.
- Apply data protection, privacy, and lawful processing requirements.
- Apply AI controls: data governance, testing, and human oversight.
- Apply bias testing, explainability, and model documentation practice.
- Apply AI monitoring, drift detection, and incident response.
- Apply AI assurance, audit, and governance reporting.
Course Methodology
- Interactive sessions built on AI governance, risk, and compliance cases.
- Workshops on AI risk assessment, control design, and regulatory mapping.
- Group exercises on AI Act classification, data protection, and incident handling.
- Hands-on practice with AI registers, control matrices, and compliance logs.
- Simulation of an AI governance review and an AI incident response.
- Facilitator coaching and peer review on personal AI GRC plans.
Who Should Take This Course
- AI governance, risk, and compliance professionals.
- Compliance officers and data protection officers.
- Risk managers and internal auditors covering AI.
- Data scientists and ML engineers with governance duties.
- Technology and information security managers.
- Consultants advising on AI GRC frameworks and readiness.
Artificial Intelligence (AI) Governance, Risk and Compliance Course Outlines
AI Fundamentals and Lifecycle
- • AI, machine learning, and generative AI concepts.
- • AI lifecycle: design, data, training, deployment, monitoring.
- • Capabilities, limitations, and failure modes.
- • Third-party models and foundation model considerations.
- • Regulatory landscape and emerging AI obligations.
- • AI use cases and value versus exposure.
- • Case discussion: an AI model that worked in testing and failed in production.
AI Governance Structures and Frameworks
- • NIST AI Risk Management Framework and its functions.
- • ISO/IEC 42001 AI management system requirements.
- • OECD AI principles and sector guidance.
- • AI governance structures, councils, and accountability.
- • AI policy, use case intake, and approval gates.
- • AI register and inventory management.
- • Workshop: designing an AI governance framework.
AI Risk Assessment and Regulatory Compliance
- • AI risk taxonomy: model, data, ethical, legal, operational.
- • Risk assessment methods and impact evaluation.
- • EU AI Act risk tiers and associated obligations.
- • Data protection, privacy, and lawful basis for AI processing.
- • Sector regulation and supervisory expectations.
- • AI compliance obligations register and mapping.
- • Exercise: classifying an AI use case under the EU AI Act.
AI Controls, Testing, and Human Oversight
- • Data governance, quality, and provenance controls.
- • Model development standards and documentation.
- • Bias testing, fairness metrics, and explainability.
- • Human oversight, review gates, and override design.
- • Access control, logging, and traceability for AI systems.
- • Testing, validation, and red teaming practice.
- • Simulation: testing an AI model for bias and explainability.
Monitoring, Assurance, and Reporting
- • AI monitoring, drift detection, and performance review.
- • AI incident response, escalation, and disclosure.
- • AI assurance approaches and internal audit over AI.
- • Third-party and supplier AI risk management.
- • AI governance reporting to boards and regulators.
- • Continuous improvement and AI GRC maturity.
- • Capstone: presenting an AI governance, risk and compliance framework.
Conclusion
By successfully completing Artificial Intelligence (AI) Governance, Risk and Compliance, participants will gain a practical command of AI fundamentals and lifecycle, AI governance frameworks including NIST AI RMF and ISO/IEC 42001, AI risk assessment and EU AI Act compliance, AI controls, testing and human oversight, and AI monitoring, assurance and governance reporting. They will acquire skills in AI governance structuring, AI risk assessment, AI control and testing design, and AI assurance reporting that keep AI systems accountable and compliant. Gentex Training Center designs the programme to help professionals govern AI with confidence.
FAQs for the Artificial Intelligence (AI) Governance, Risk and Compliance course
— What is the "Artificial Intelligence (AI) Governance, Risk and Compliance" course about?
It prepares professionals to govern AI across its full lifecycle. The course spans AI fundamentals and lifecycle, AI governance structures and frameworks, AI risk assessment and regulatory compliance, AI controls, testing and human oversight, and monitoring, assurance and reporting.
— What are the key benefits of the "Artificial Intelligence (AI) Governance, Risk and Compliance" course?
You gain a practical command of AI GRC that links the AI systems an organisation deploys with the governance, risk, and compliance discipline that keeps them accountable. The course improves governance structuring and regulatory mapping, strengthens AI risk assessment and control design, sharpens bias testing and oversight practice, and gives you AI assurance and reporting that stands up to regulators and audit.
— What skills will I gain from the "Artificial Intelligence (AI) Governance, Risk and Compliance" course?
You will develop four core skills: AI governance framework application with NIST AI RMF and ISO/IEC 42001, AI risk assessment across model, data and ethical risk, AI regulatory compliance with EU AI Act classification and data protection, and AI control, testing, monitoring and assurance reporting.
— What tools, methods, or standards are covered in the "Artificial Intelligence (AI) Governance, Risk and Compliance" course?
The course covers AI, machine learning and generative AI concepts, the AI lifecycle across design, data, training, deployment and monitoring, capabilities, limitations and failure modes, third-party models and foundation model considerations, the regulatory landscape and emerging AI obligations, the NIST AI Risk Management Framework and its functions, ISO/IEC 42001 AI management system requirements, OECD AI principles and sector guidance, AI governance structures, councils and accountability, AI policy, use case intake and approval gates, AI register and inventory management, the AI risk taxonomy across model, data, ethical, legal and operational risk, risk assessment methods and impact evaluation, EU AI Act risk tiers and associated obligations, data protection, privacy and lawful basis for AI processing, sector regulation and supervisory expectations, AI compliance obligations register and mapping, data governance, quality and provenance controls, model development standards and documentation, bias testing, fairness metrics and explainability, human oversight, review gates and override design, access control, logging and traceability for AI systems, testing, validation and red teaming, AI monitoring, drift detection and performance review, AI incident response, escalation and disclosure, AI assurance approaches and internal audit over AI, third-party and supplier AI risk management, and AI governance reporting to boards and regulators.
— How is the "Artificial Intelligence (AI) Governance, Risk and Compliance" course applied in real-world practice?
Participants apply it to real work such as designing an AI governance framework, building an AI register and use case intake process, classifying an AI use case under the EU AI Act, assessing model and data risk for a deployed system, running bias and explainability testing, handling an AI incident and escalation, auditing AI controls, and presenting an AI GRC framework. The same methods support data protection impact assessments and regulator-facing AI reporting.
Contact our team and let us help you find the right option.
Introduction
Artificial Intelligence (AI) Governance, Risk and Compliance Training Course prepares professionals to govern AI across its full lifecycle. Participants work through AI fundamentals and lifecycle stages, AI governance structures and accountability, the NIST AI RMF and ISO/IEC 42001, AI risk assessment across model, data, and ethical risk, AI regulatory compliance including the EU AI Act and data protection law, AI controls and testing, and AI monitoring, assurance, and reporting as one connected discipline. The programme links the AI systems an organisation deploys with the governance, risk, and compliance discipline that keeps them accountable. As a result, learners gain a practical command of AI GRC. Furthermore, the course strengthens AI risk judgement, control design, and compliance literacy.
Artificial Intelligence (AI) Governance, Risk and Compliance Course Objectives
- Explain AI concepts, capabilities, and lifecycle stages.
- Apply AI governance structures, policies, and accountability models.
- Apply the NIST AI RMF and ISO/IEC 42001 requirements.
- Apply the EU AI Act risk classification and obligations.
- Apply AI risk assessment across model, data, ethical, and legal risk.
- Apply data protection, privacy, and lawful processing requirements.
- Apply AI controls: data governance, testing, and human oversight.
- Apply bias testing, explainability, and model documentation practice.
- Apply AI monitoring, drift detection, and incident response.
- Apply AI assurance, audit, and governance reporting.
Course Methodology
- Interactive sessions built on AI governance, risk, and compliance cases.
- Workshops on AI risk assessment, control design, and regulatory mapping.
- Group exercises on AI Act classification, data protection, and incident handling.
- Hands-on practice with AI registers, control matrices, and compliance logs.
- Simulation of an AI governance review and an AI incident response.
- Facilitator coaching and peer review on personal AI GRC plans.
Who Should Take This Course
- AI governance, risk, and compliance professionals.
- Compliance officers and data protection officers.
- Risk managers and internal auditors covering AI.
- Data scientists and ML engineers with governance duties.
- Technology and information security managers.
- Consultants advising on AI GRC frameworks and readiness.
Artificial Intelligence (AI) Governance, Risk and Compliance Course Outlines
AI Fundamentals and Lifecycle
- • AI, machine learning, and generative AI concepts.
- • AI lifecycle: design, data, training, deployment, monitoring.
- • Capabilities, limitations, and failure modes.
- • Third-party models and foundation model considerations.
- • Regulatory landscape and emerging AI obligations.
- • AI use cases and value versus exposure.
- • Case discussion: an AI model that worked in testing and failed in production.
AI Governance Structures and Frameworks
- • NIST AI Risk Management Framework and its functions.
- • ISO/IEC 42001 AI management system requirements.
- • OECD AI principles and sector guidance.
- • AI governance structures, councils, and accountability.
- • AI policy, use case intake, and approval gates.
- • AI register and inventory management.
- • Workshop: designing an AI governance framework.
AI Risk Assessment and Regulatory Compliance
- • AI risk taxonomy: model, data, ethical, legal, operational.
- • Risk assessment methods and impact evaluation.
- • EU AI Act risk tiers and associated obligations.
- • Data protection, privacy, and lawful basis for AI processing.
- • Sector regulation and supervisory expectations.
- • AI compliance obligations register and mapping.
- • Exercise: classifying an AI use case under the EU AI Act.
AI Controls, Testing, and Human Oversight
- • Data governance, quality, and provenance controls.
- • Model development standards and documentation.
- • Bias testing, fairness metrics, and explainability.
- • Human oversight, review gates, and override design.
- • Access control, logging, and traceability for AI systems.
- • Testing, validation, and red teaming practice.
- • Simulation: testing an AI model for bias and explainability.
Monitoring, Assurance, and Reporting
- • AI monitoring, drift detection, and performance review.
- • AI incident response, escalation, and disclosure.
- • AI assurance approaches and internal audit over AI.
- • Third-party and supplier AI risk management.
- • AI governance reporting to boards and regulators.
- • Continuous improvement and AI GRC maturity.
- • Capstone: presenting an AI governance, risk and compliance framework.
Conclusion
By successfully completing Artificial Intelligence (AI) Governance, Risk and Compliance, participants will gain a practical command of AI fundamentals and lifecycle, AI governance frameworks including NIST AI RMF and ISO/IEC 42001, AI risk assessment and EU AI Act compliance, AI controls, testing and human oversight, and AI monitoring, assurance and governance reporting. They will acquire skills in AI governance structuring, AI risk assessment, AI control and testing design, and AI assurance reporting that keep AI systems accountable and compliant. Gentex Training Center designs the programme to help professionals govern AI with confidence.
FAQs for the Artificial Intelligence (AI) Governance, Risk and Compliance course
— What is the "Artificial Intelligence (AI) Governance, Risk and Compliance" course about?
It prepares professionals to govern AI across its full lifecycle. The course spans AI fundamentals and lifecycle, AI governance structures and frameworks, AI risk assessment and regulatory compliance, AI controls, testing and human oversight, and monitoring, assurance and reporting.
— What are the key benefits of the "Artificial Intelligence (AI) Governance, Risk and Compliance" course?
You gain a practical command of AI GRC that links the AI systems an organisation deploys with the governance, risk, and compliance discipline that keeps them accountable. The course improves governance structuring and regulatory mapping, strengthens AI risk assessment and control design, sharpens bias testing and oversight practice, and gives you AI assurance and reporting that stands up to regulators and audit.
— What skills will I gain from the "Artificial Intelligence (AI) Governance, Risk and Compliance" course?
You will develop four core skills: AI governance framework application with NIST AI RMF and ISO/IEC 42001, AI risk assessment across model, data and ethical risk, AI regulatory compliance with EU AI Act classification and data protection, and AI control, testing, monitoring and assurance reporting.
— What tools, methods, or standards are covered in the "Artificial Intelligence (AI) Governance, Risk and Compliance" course?
The course covers AI, machine learning and generative AI concepts, the AI lifecycle across design, data, training, deployment and monitoring, capabilities, limitations and failure modes, third-party models and foundation model considerations, the regulatory landscape and emerging AI obligations, the NIST AI Risk Management Framework and its functions, ISO/IEC 42001 AI management system requirements, OECD AI principles and sector guidance, AI governance structures, councils and accountability, AI policy, use case intake and approval gates, AI register and inventory management, the AI risk taxonomy across model, data, ethical, legal and operational risk, risk assessment methods and impact evaluation, EU AI Act risk tiers and associated obligations, data protection, privacy and lawful basis for AI processing, sector regulation and supervisory expectations, AI compliance obligations register and mapping, data governance, quality and provenance controls, model development standards and documentation, bias testing, fairness metrics and explainability, human oversight, review gates and override design, access control, logging and traceability for AI systems, testing, validation and red teaming, AI monitoring, drift detection and performance review, AI incident response, escalation and disclosure, AI assurance approaches and internal audit over AI, third-party and supplier AI risk management, and AI governance reporting to boards and regulators.
— How is the "Artificial Intelligence (AI) Governance, Risk and Compliance" course applied in real-world practice?
Participants apply it to real work such as designing an AI governance framework, building an AI register and use case intake process, classifying an AI use case under the EU AI Act, assessing model and data risk for a deployed system, running bias and explainability testing, handling an AI incident and escalation, auditing AI controls, and presenting an AI GRC framework. The same methods support data protection impact assessments and regulator-facing AI reporting.
Partners in Learning
Our Partners in Learning include leading companies and organizations that trust Gentex Training Center for high-quality training courses and professional development programs. Together, we deliver impactful training programs that support skills growth, leadership development, and organizational excellence.
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