Course Objective
What this programme helps you achieve
A hands-on 2 days programme that builds practical, job-ready capability in this area. Delivered through guided walkthroughs, worked examples and reusable checklists you can apply immediately.
UnderstandBuild a clear, structured understanding of the core concepts, terminology and drivers behind this domain.
ApplyTranslate the framework into practical actions, controls and working methods you can use on real engagements.
AssessEvaluate current-state maturity, identify gaps and prioritise the improvements that reduce the most risk.
DeliverProduce the artefacts, evidence and reporting expected of a competent practitioner in this area.
Market Need
Why this matters now
Responsible AI Governance and AI Risk Management addresses a capability that organisations increasingly need but often lack in a structured, defensible form. Teams frequently rely on ad-hoc knowledge, scattered documents or vendor claims instead of a repeatable method.
This programme closes that gap. It gives participants a shared vocabulary, a practical method and a set of reusable artefacts so the organisation can operate this domain consistently rather than reactively.
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Framework alignment: NIST AI RMF, ISO/IEC 42001 and ISO/IEC 23894 AI risk guidance.
What makes this programme useful
01
Practical Focus
Every module is grounded in real scenarios, walkthroughs and reusable checklists rather than theory alone.
02
Career Relevant
Skills map directly to in-demand roles and to how organisations actually operate this domain.
03
Structured Path
A logical progression from fundamentals to applied practice, with a clear takeaway toolkit.
Key Learning Areas
What participants will learn
Responsible AI principles: fairness, accountability, transparency
AI governance operating models and roles
AI risk taxonomy: safety, bias, privacy, security, misuse
AI risk assessment and impact assessment methods
NIST AI RMF and ISO/IEC 42001/23894 alignment
Model documentation, model cards and datasheets
Human oversight, escalation and AI incident handling
Building an enterprise AI governance and risk programme
2 Days Agenda
Structured learning flow
Day 1
Day 1 — Responsible AI & Governance
Responsible AI principles: fairness, accountability, transparency; AI governance operating models and roles; AI risk taxonomy: safety, bias, privacy, security, misuse; AI risk assessment and impact assessment methods.
Day 2
Day 2 — AI Risk Assessment & Programme
NIST AI RMF and ISO/IEC 42001/23894 alignment; Model documentation, model cards and datasheets; Human oversight, escalation and AI incident handling; Building an enterprise AI governance and risk programme.
Audience & Entry Level
Who should attend?
This programme is designed for professionals who work with, or are moving into, this area of cybersecurity, governance or technology.
AI Governance LeadRisk & ComplianceData Science / ML TeamSecurity ArchitectProduct & EngineeringCISO / CTOIT AuditorLegal & Privacy
Recommended prerequisite
A general awareness of information security or IT concepts is helpful. No specific certification is required to attend.
Learning Outcome
What participants should be able to do after the course
- Confidently work with responsible ai principles: fairness, accountability, transparency.
- Confidently work with ai governance operating models and roles.
- Confidently work with ai risk taxonomy: safety, bias, privacy, security, misuse.
- Confidently work with ai risk assessment and impact assessment methods.
- Confidently work with nist ai rmf and iso/iec 42001/23894 alignment.