Responsible AI Governance & Risk Management

Categories: Artificial Intelligence & Data

About Course

3-Day Intensive Live Online Training Programme

Upcoming dates: Available on request.

Responsible AI Governance & Risk Management is an intensive, instructor‑led training programme designed for organisations that are deploying AI and need practical tools to govern it safely, ethically, and in line with regulation. Participants work through real‑world scenarios, risk assessments, and governance exercises to build confidence in managing AI across their organisation.

Through a mix of short briefings, group work, case studies, and implementation clinics, attendees learn how to apply leading AI governance and risk frameworks in practice, rather than just hearing about concepts. By the end of the course, they leave with draft artefacts (risk registers, governance actions, policy outlines) they can adapt and use in their own workplace.

Participants who successfully complete the training will receive a Certificate of Completion from London Global Centre. Training is delivered by experienced trainers with strong professional and industry backgrounds.

Explain the core principles of responsible AI and AI governance to stakeholders in clear, non‑technical language.
Identify and classify AI risks across ethical, operational, legal, security, and reputational dimensions using structured frameworks.
Apply recognised AI risk management frameworks (e.g. NIST AI RMF concepts, ISO/IEC‑style governance principles) to practical scenarios.
Design or refine basic AI governance structures: roles, responsibilities, oversight committees, and escalation routes.
Review AI initiatives and use cases using simple assessment tools (e.g. risk scoring or traffic‑light models) to inform go/no‑go or “go with conditions” decisions.
Draft or improve key AI governance artefacts such as AI use guidelines, risk registers, and monitoring checklists.

Course Content

Day 1: Foundations, Principles & Risk Landscape

Interactive briefing: why responsible AI matters now (risk, regulation, reputation, value).
Overview of responsible AI principles (fairness, reliability & safety, privacy & security, inclusiveness, transparency, accountability).
Exercise: map current or planned AI use in your organisation (or scenario) – where is AI already influencing decisions?
Types of AI risks: ethical, operational, regulatory, security, data, and societal impact.
Stakeholder mapping: who is accountable, who is impacted, and who needs to be involved in governance.
Workshop: small‑group risk identification for a given AI case (participants work through a structured checklist and present key risks).

Day 2: Frameworks, Risk Assessment & Controls

Guided walkthrough of practical AI risk management frameworks (e.g. NIST AI RMF concepts, ISO/IEC 42001‑style governance themes).
Exercise: using a simple “red / amber / green” risk assessment model to evaluate example AI use cases.
Group discussion: what thresholds, guardrails, or conditions would you set for high‑risk use cases?
Overview of key regulatory and compliance themes (EU AI Act direction of travel, data protection, auditability, documentation expectations – non‑legal briefing).
Policy and controls workshop: draft or review AI guidelines, acceptable use rules, and human‑in‑the‑loop checks, identify gaps in current controls and monitoring.
Hands‑on exercise: participants work in teams to design a basic AI governance control set for a sample use case (e.g. automated decision system) and present it to the “board”.

Day 3: Organisational Implementation & Governance Clinics

Governance operating model: roles (board, executives, risk, compliance, IT, data, product), committees, and decision gateways.
Exercise: design a simple AI governance process for one AI product or initiative – from idea to approval, deployment, and monitoring.
Discussion: integrating AI governance into existing risk, compliance, and audit processes rather than creating a silo.
“Governance Lab”: participants work on a mini‑project using either: one of their organisation’s real AI use cases (for corporate groups), or a realistic, sector‑specific case provided by the trainer.
Guided clinic: facilitator supports each group to: assess risk level, propose governance and oversight mechanisms, outline policies/controls and monitoring approach, prepare a short governance summary for senior stakeholders.
Final action planning: each participant defines 3–5 concrete actions to take back into their role (e.g. “establish an AI risk inventory”, “run a governance workshop with our product team”).

A course by

London Global Centre

Responsible AI Governance & Risk Management

About Course

What Will You Learn?

Course Content

Day 1: Foundations, Principles & Risk Landscape

Interactive briefing: why responsible AI matters now (risk, regulation, reputation, value).

Overview of responsible AI principles (fairness, reliability & safety, privacy & security, inclusiveness, transparency, accountability).

Exercise: map current or planned AI use in your organisation (or scenario) – where is AI already influencing decisions?

Types of AI risks: ethical, operational, regulatory, security, data, and societal impact.

Stakeholder mapping: who is accountable, who is impacted, and who needs to be involved in governance.

Workshop: small‑group risk identification for a given AI case (participants work through a structured checklist and present key risks).

Day 2: Frameworks, Risk Assessment & Controls

Guided walkthrough of practical AI risk management frameworks (e.g. NIST AI RMF concepts, ISO/IEC 42001‑style governance themes).

Exercise: using a simple “red / amber / green” risk assessment model to evaluate example AI use cases.

Group discussion: what thresholds, guardrails, or conditions would you set for high‑risk use cases?

Overview of key regulatory and compliance themes (EU AI Act direction of travel, data protection, auditability, documentation expectations – non‑legal briefing).

Policy and controls workshop: draft or review AI guidelines, acceptable use rules, and human‑in‑the‑loop checks, identify gaps in current controls and monitoring.

Hands‑on exercise: participants work in teams to design a basic AI governance control set for a sample use case (e.g. automated decision system) and present it to the “board”.

Day 3: Organisational Implementation & Governance Clinics

Governance operating model: roles (board, executives, risk, compliance, IT, data, product), committees, and decision gateways.

Exercise: design a simple AI governance process for one AI product or initiative – from idea to approval, deployment, and monitoring.

Discussion: integrating AI governance into existing risk, compliance, and audit processes rather than creating a silo.

“Governance Lab”: participants work on a mini‑project using either: one of their organisation’s real AI use cases (for corporate groups), or a realistic, sector‑specific case provided by the trainer.

Guided clinic: facilitator supports each group to: assess risk level, propose governance and oversight mechanisms, outline policies/controls and monitoring approach, prepare a short governance summary for senior stakeholders.

Final action planning: each participant defines 3–5 concrete actions to take back into their role (e.g. “establish an AI risk inventory”, “run a governance workshop with our product team”).

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