AI Governance Professional Training Course – New York, 22–26 Feb 2027

AI Governance Professional is a 1-week training course for governance, risk, compliance and data protection professionals overseeing artificial intelligence, who leave with a draft AI governance charter. Boards, auditors and customers now ask who answers for each model in use, so organisations need clear accountability before AI systems scale. The course in AI governance is delivered by Jupiter Prime Training.

Why AI Governance Matters Now

Three pressures shape AI governance today: new binding rules on high-risk AI uses, rapid uptake of generative AI tools by staff outside any approval route, and boards and auditors asking for evidence that models are fair, explainable and monitored. An organisation that governs AI well can approve new use cases quickly and defend them under scrutiny. One that governs it badly faces hidden models, biased decisions and data protection breaches. The course rests on ISO/IEC 42001, the international standard for an AI management system (AIMS), and the NIST AI Risk Management Framework (NIST AI RMF).

Who Gains Most from AI Governance

• Governance officers who design oversight committees and escalation routes for AI systems
• Risk specialists who classify AI use cases and maintain the risk register
• Compliance professionals who map AI obligations into policies, controls and assurance evidence
• Data protection practitioners who assess privacy effects of model inputs and outputs
• Internal auditors who test whether AI controls operate as designed

A second-line risk and compliance function gains most when its governance, privacy and audit members attend together.

AI Governance Capabilities

Participants turn trustworthy AI principles into an inventory, a classification method and testable controls.

• Build an AI system inventory with owners and purposes — the oversight committee
• Apply a risk classification scheme to rank use cases — approval decisions
• Design ISO/IEC 42001 clauses into existing policy, roles and management review — the board
• Assess model documentation, data lineage and explainability evidence — internal and external auditors
• Specify human oversight, monitoring and incident triggers for high-impact models — business system owners
• Communicate residual AI risk and control gaps — executive committee

Key AI Governance Terms

• AI management system (AIMS) — the policies, roles, processes and records an organisation uses to direct and control its AI activities.
• AI impact assessment — a structured review of how an AI system could affect individuals, groups and the organisation.
• Model card — a short document recording a model's purpose, training data, performance limits and intended users.
• Human oversight — the ability of a named person to monitor, override or stop an AI system's output.
• Algorithmic bias — systematic unfair outcomes for certain groups caused by data, design or deployment choices.
• Statement of Applicability — the ISO/IEC 42001 record of which controls apply to an organisation and why others are excluded.

AI Governance Day by Day

Day 1: The Accountability Landscape for AI

• Trustworthy AI Principles — Fairness, Transparency, Safety and Accountability Trade-Offs
• Emerging AI Rules Landscape — Risk-Tiered Obligations Across Jurisdictions
• Three Lines Model for AI — Who Owns, Checks and Assures
• AI System Inventory — Discovering Shadow Models and Generative Tools
• Board Reporting Expectations — What Directors Need to See

Day 2: Frameworks and Management Systems

• ISO/IEC 42001 Clauses — Context, Leadership, Planning and Support
• ISO/IEC 42001 Annex Controls — Building the Statement of Applicability
• NIST AI RMF Functions — Govern, Map, Measure and Manage
• Framework Crosswalk — Aligning AI Controls with Privacy and Security Systems
• AI Policy Architecture — Acceptable Use, Procurement and Development Rules

Day 3: Classifying and Assessing AI Risk

• Risk Classification Matrix — Prohibited, High, Limited and Minimal Impact Tiers
• AI Impact Assessment Template — Effects on Individuals and Groups
• Bias and Fairness Testing Evidence — Questions for Data Science Teams
• Third-Party and Vendor AI Due Diligence — Contract and Assurance Clauses
• AI Risk Register — Likelihood, Impact and Treatment Owners

Day 4: Controls, Documentation and Oversight

• Model Cards and Datasheets — Minimum Documentation per Risk Tier
• Human-in-the-Loop Design — Override, Escalation and Stop Rights
• Post-Deployment Monitoring — Drift, Incident and Complaint Triggers
• AI Incident Response Playbook — Containment, Notification and Lessons
• Control Testing and Audit Trails — Evidence an Assurance Review Accepts

Day 5: Practising the AI Governance Charter

• Oversight Committee Terms of Reference — Applying Three Lines Roles
• Use-Case Approval Workflow — Applying the Classification Matrix
• Charter Control Set — Applying ISO/IEC 42001 and NIST AI RMF Mapping
• Executive Briefing on Residual AI Risk — Framing the Ask
• AI Governance Charter — Scope, Roles, Approval Gates and Review Cycle

Applying AI Governance After the Course

• Lead an AI inventory sprint across two business units, classifying each system with the risk matrix and naming its owner.
• Run one AI impact assessment on a live use case, such as a credit scoring model or a patient triage chatbot, and log the results in the risk register.

AI Governance Questions (FAQ)

What is AI governance professional practice and why does it matter now?

AI governance professional practice is the discipline of directing, controlling and assuring AI systems across their lifecycle. It matters now because AI rules are tightening, staff adopt generative tools quickly, and boards want evidence that models are fair, safe and accountable.

Which roles gain most from AI governance professional training?

Governance officers, risk specialists, compliance professionals, data protection practitioners and internal auditors gain most. You benefit when you approve AI use cases, write AI policy or test AI controls.

How does AI governance professional training differ from a data science or AI strategy course?

AI governance professional training focuses on oversight, risk classification, documentation and control assurance. A data science course teaches model building, and an AI strategy course centres on value and investment choices rather than accountability and evidence.

What do you bring back from AI governance professional training?

You bring back a draft charter that sets AI oversight roles, approval gates and review cycles. It links an AI inventory, a risk classification method and a control set mapped to ISO/IEC 42001 and NIST AI RMF.

AI Governance: The Way Forward

The AI governance charter first informs which AI use cases need committee approval before launch. Risk, compliance, privacy and audit leads use it to share one register and one set of approval gates. After the first quarter of approvals, revisit the classification thresholds and monitoring triggers, since real cases show where tiers are too strict or loose.