AI Governance Professional

This course is suitable for AI Governance Professionals, CIOs, CISOs, Chief Data Officers, Chief Privacy Officers, Data Protection Officers, AI Risk Managers, Enterprise Risk Professionals, GRC Professionals, Information Security Managers, Privacy Professionals, Compliance Officers, AI Project Managers, Internal Auditors, and professionals involved in AI oversight or assurance.

Participants learn how to establish AI governance structures, understand responsible and trustworthy AI principles, identify and assess AI risks, interpret relevant laws and standards, govern AI development activities, manage deployment risks, implement human oversight, monitor AI performance, and support continuous improvement across the AI life cycle.

Yes. The course covers key elements of the EU AI Act, including risk-based AI classification, prohibited AI practices, high-risk AI systems, transparency obligations, general-purpose AI models, provider and deployer responsibilities, conformity assessment, technical documentation, human oversight, post-market monitoring, and AI incident reporting.

Yes. The curriculum includes ISO/IEC 42001 AI Management System concepts as part of the broader AI governance standards and frameworks landscape. This helps participants understand how structured management-system approaches can support responsible organizational governance of AI.

The course includes coverage of ISO/IEC 42001, ISO/IEC 23894 for AI risk management, the NIST AI Risk Management Framework, OECD AI Principles, and AI governance tools and assessment methods. It also explores how these frameworks can support practical enterprise AI governance.

Yes. Responsible and Trustworthy AI are core areas of the program. Topics include fairness, non-discrimination, transparency, explainability, accountability, privacy, data protection, security, resilience, and appropriate human oversight.

The training addresses risks such as AI bias and discrimination, privacy violations, cybersecurity threats, lack of transparency, explainability limitations, model hallucinations, intellectual property concerns, third-party AI risks, automated decision-making risks, regulatory non-compliance, operational risks, reputational risks, model drift, data drift, and insufficient human oversight.

Yes. The course covers Generative AI and LLM governance topics such as hallucination management, prompt injection risks, Retrieval-Augmented Generation governance, fine-tuning governance, prompt governance, AI output verification, enterprise GenAI use, employee use of AI, and Shadow AI.

The course takes a full life-cycle approach covering AI project initiation, intended purpose, risk assessment, data sourcing, training data governance, model selection, design controls, testing, validation, approval, deployment, continuous monitoring, change management, retraining, incident management, model retirement, and decommissioning.

Yes. Participants are introduced to AI design risk assessments, AI impact assessments, privacy impact assessments, algorithmic impact assessments, deployment risk evaluations, bias and fairness assessments, security assessments, explainability assessments, and risk acceptance considerations.

AI development governance includes business justification, intended purpose, system boundaries, stakeholder identification, responsible AI requirements, foreseeable misuse, data provenance, ownership, licensing, quality, representativeness, dataset lineage, bias management, model versioning, reproducibility, privacy-by-design, security-by-design, explainability-by-design, testing, validation, independent review, and release approval.

Yes. The training covers human-in-the-loop, human-on-the-loop, and human-in-command approaches. It also addresses human intervention, override mechanisms, prevention of automation bias, accountability, and appropriate oversight of AI-assisted or automated decisions.

The training can strengthen capabilities relevant to roles in AI Governance, Responsible AI, AI Risk Management, GRC, Privacy, Compliance, Information Security, AI Assurance, Internal Audit, Model Risk, AI Policy, and enterprise AI oversight. It is particularly useful for professionals whose organizations are adopting AI and require structured governance and risk management.

IEVISION IT SERVICES offers live instructor-led learning with a practical and case study-based approach. The published course highlights experienced industry trainers, practical learning, virtual instructor-led delivery, and exam preparation support, while the curriculum addresses governance, risk, regulations, standards, development, deployment, monitoring, and assurance. (IEVISION)