Live Online Practical AI Governance, Responsible AI & ISO 42001 Training Course
Live online, instructor-led · next cohort Oct 19, 2026 · from USD 1,700
Governance frameworks only protect an organisation when someone can turn them into a completed register, a tested model and a signed-off record. Ten days of workbench sessions build that ability, using templates and open-source tools you can keep using afterwards. You catalogue AI systems in an inventory workbook, score risks with a matrix based on ISO/IEC 23894 and the NIST AI Risk Management Framework, complete an AI system impact assessment and draft a Statement of Applicability against the Annex A controls of ISO/IEC 42001.
Later sessions are tool-led: measuring group fairness with Fairlearn, explaining predictions with SHAP and LIME, writing model cards and datasheets, logging models in MLflow, tracking drift with Evidently and probing a chatbot with adversarial prompts. Notebooks are pre-built, so analysts without coding experience can follow every step. Risk and compliance officers, data protection officers, data analysts and scientists, IT auditors, product managers and ICT staff in banking, health, government and NGOs gain a toolkit they can apply straight away. Attend in the classroom, online or in-house, and earn a CPD-accredited certificate.
At a Glance
How Live Online Training Works
Live online training, led by a trainer in real time, for participants anywhere.
More about live online training →- Format
- Live online, instructor-led
- Duration
- Mon - Fri (10 Days)
- Fee
- USD 1,700 (KES 160,000)
- Next start dates
- Oct 19, 2026, Nov 16, 2026, Dec 14, 2026
- Certificate
- Certificate of Completion
- What you need
- A computer with a reliable internet connection, a webcam and a headset
About this course
Many professionals asked to oversee AI understand the principles but have never filled in an impact assessment, read a fairness report or checked a model's documentation against a standard. That gap matters. Regulators, auditors and certification bodies look for records and test results, not intentions, and both ISO/IEC 42001 and the EU AI Act expect evidence that risks were assessed and controls actually operated.
The Practical AI Governance, Responsible AI & ISO 42001 Training Course is organised as a sequence of build sessions. In the first week participants set up the core governance records for a sample organisation: inventory, policy, scope, risk register, impact assessment and control selection. In the second week they work with the technical evidence behind those records, testing a credit-scoring model for bias, generating explanations, documenting models and datasets, configuring drift reports, red teaming a generative AI assistant and finally assembling everything into an audit-ready evidence file.
Every day follows the same rhythm of short demonstration, guided build and peer review. Templates are supplied in spreadsheet and document form, and the Python notebooks run in a browser with prepared datasets, so participants concentrate on interpreting results rather than writing code.
Training Objectives
Working through the hands-on sessions, participants will be able to:
- Build and maintain an AI system inventory that records ownership, purpose, data sources and risk tier.
- Complete an AI risk assessment and treatment plan using a scoring matrix aligned with ISO/IEC 23894.
- Carry out an AI system impact assessment and record its findings for decision makers.
- Draft a Statement of Applicability that justifies the inclusion or exclusion of ISO/IEC 42001 Annex A controls.
- Measure fairness across groups with Fairlearn and interpret the results for non-technical colleagues.
- Explain model predictions with SHAP and LIME and record known limits in a model card.
- Configure drift and performance monitoring and write the alert and response rules.
- Assemble an audit-ready evidence file and test it in a mock internal audit.
What you'll learn
Key Takeaways
Participants go home with a working toolkit, including:
- A populated AI inventory workbook that can be reused in their own organisation.
- A risk register and treatment plan with scored, justified entries.
- A completed impact assessment template with worked examples of findings.
- A draft Statement of Applicability for the ISO/IEC 42001 Annex A controls.
- Reusable Python notebooks for fairness testing and model explanation.
- A model card and datasheet written for a sample credit-scoring model.
- A generative AI red-teaming log and acceptable use checklist.
- An indexed evidence file ready for internal audit review.
Training Methodology
Every session is built around doing the work: the trainer demonstrates each step, then participants repeat it on their own files. The course combines:
- Guided builds using supplied spreadsheet and document templates
- Browser-based Python notebooks with prepared datasets, usable without prior coding
- A single sample organisation carried through every exercise
- Pair work in which one participant builds and another reviews
- Red-teaming and incident tabletop sessions with live scenarios
- A mock internal audit of each participant's evidence file
Day 1: Setting Up the Governance Workbench
- Tour of the toolkit: workbooks, document templates and browser-based notebooks
- AI terminology from ISO/IEC 22989 in plain language
- Finding where AI already operates, including features inside purchased software
- Inventory fields: owner, purpose, data, users and decisions affected
- Turning responsible AI principles into review questions
- Introducing the sample organisation used throughout the course
- Hands-on: populating the inventory with ten sample systems
Day 2: Policy, Roles and Scope in Practice
- Drafting a one-page AI policy from a structured template
- Assigning roles and decision rights with a RACI chart
- Generative AI acceptable use rules that staff will actually follow
- Defining context and scope of an AI management system under clauses 4.1 to 4.3
- Recording interested parties and their requirements
- Linking AI governance to existing ISO/IEC 27001 or privacy programmes
- Hands-on: scope statement and interested parties register for the sample organisation
Day 3: Building the AI Risk Register
- Risk sources for AI systems, drawing on ISO/IEC 23894 and Annex C of ISO/IEC 42001
- Using the Map and Measure functions of the NIST AI RMF to frame risks
- Designing a likelihood and impact scoring matrix
- Writing risk statements that name cause, event and consequence
- Selecting treatments and recording residual risk
- Spreadsheet techniques: drop-down lists, heat maps and filters
- Hands-on: risk register for three high-priority use cases
Day 4: AI System Impact Assessment
- When an impact assessment is required and who should lead it
- Identifying affected individuals, groups and communities
- Assessing effects on rights, safety, access to services and livelihoods
- Gathering input from users and affected people
- Guidance from ISO/IEC 42005 and the fundamental rights impact assessment under the EU AI Act
- Recording mitigations and decision outcomes
- Hands-on: impact assessment of an automated loan screening tool
Day 5: Statement of Applicability and Control Mapping
- The structure of Annex A control objectives and controls
- Using the implementation guidance in Annex B
- Deciding which controls apply and documenting justifications
- Mapping to existing ISO/IEC 27001 and privacy controls to avoid duplication
- Linking each control to risks in the register
- Building a control evidence tracker
- Hands-on: draft Statement of Applicability and week one peer review
Day 6: Bias Testing Lab
- Sources of bias in training data, labels and proxy variables
- Group fairness metrics: demographic parity, equal opportunity and equalised odds
- Running Fairlearn MetricFrame analyses on a credit-scoring dataset
- Cross-checking results with IBM's AI Fairness 360 toolkit
- Mitigation options before, during and after model training
- Writing a fairness test report for a non-technical committee
- Hands-on: test, mitigate and re-test a biased model
Day 7: Explainability and Model Documentation Lab
- Global versus local explanations and their limits
- SHAP values for feature contributions
- LIME for explaining individual predictions
- Writing model cards and datasheets for datasets
- Logging models, parameters and metrics in MLflow
- Explaining a decision to an affected customer in plain language
- Hands-on: model card and explanation pack for the sample model
Day 8: Generative AI Testing and Controls
- How large language models fail: hallucination, leakage, bias and prompt injection
- Building a red-teaming prompt library and test log
- Evaluating outputs for accuracy, harmful content and data exposure
- Configuring system prompts, retrieval sources and output filters
- Controls drawn from the NIST Generative AI Profile
- Disclosure, labelling and human review of generated content
- Hands-on: red teaming a sample customer service assistant
Day 9: Monitoring, Incidents and Internal Audit
- Setting up drift and performance reports with Evidently
- Choosing thresholds and alert routes
- AI incident log, severity scale and response playbook
- An internal audit checklist built from clauses 4 to 10 and Annex A
- Sampling evidence and writing audit findings
- Corrective action records and follow-up
- Hands-on: drift report review and incident tabletop
Day 10: Integrated Project: The Audit-Ready Evidence File
- Assembling records from the previous nine days into one indexed file
- Cross-checking risks, controls, tests and monitoring for consistency
- Mock internal audit by a peer team
- Fixing the findings raised
- Adapting the toolkit for your own organisation
- Presentation of evidence files and trainer feedback
- Personal checklist for the first 30 days back at work
The course is built for hands-on practitioners who will produce or review governance records, such as:
- Risk officers and compliance analysts asked to review AI use cases
- Data protection officers and privacy analysts
- Data analysts, data scientists and machine learning engineers
- IT auditors and internal audit staff assessing AI controls
- Product managers and business analysts introducing AI features
- Information security analysts and ISO/IEC 27001 implementers
- Quality and documentation officers supporting certification projects
- ICT and digital officers in ministries, local authorities and public agencies
- Monitoring, evaluation and data staff in NGOs using predictive or generative tools
Completing the hands-on labs, attending every session and submitting the final evidence file earns each participant a CPD-accredited Certificate of Completion issued by Vision Reach Global Consultancy.
The certificate attests to the training and practical work completed. It does not certify an organisation to ISO/IEC 42001 or qualify the holder as a lead auditor, and participants keep the full toolkit they built.
Choose Where You Want to Learn
Practical AI Governance, Responsible AI & ISO 42001 Training Course Venues
KenyaNairobiNext Nov 9· 13 dates$3,000- KenyaMombasaNext Oct 26· 13 dates$3,200
- KenyaNaivashaNext Nov 2· 13 dates$3,000
- KenyaKisumuNext Oct 26· 13 dates$3,000
- KenyaMachakosNext Nov 2· 13 dates$3,200
UgandaKampalaNext Nov 2· 13 dates$3,800
RwandaKigaliNext Oct 26· 13 dates$3,800
TanzaniaArushaNext Nov 9· 13 dates$3,800- TanzaniaDodomaNext Oct 26· 13 dates$3,600
- TanzaniaDar es SalaamNext Oct 26· 13 dates$4,000
- TanzaniaZanzibarNext Nov 16· 13 dates$5,200
NigeriaLagosNext Nov 16· 13 dates$5,600- NigeriaAbujaNext Nov 9· 13 dates$5,600
South AfricaPretoriaNext Nov 2· 13 dates$6,400- South AfricaJohannesburgNext Nov 2· 13 dates$6,800
- South AfricaCape TownNext Oct 26· 13 dates$7,600
UAEDubaiNext Oct 26· 13 dates$7,600
GhanaAccraNext Nov 16· 13 dates$9,000
Scroll sideways to see all 18 cities →
Frequently Asked Questions About Live Online Practical AI Governance, Responsible AI & ISO 42001 Training Course
Everything you need to know about this course before you register.
By the end of the Practical AI Governance, Responsible AI & ISO 42001 programme, you'll be able to build and maintain an ai system inventory that records ownership, purpose, data sources and risk tier, complete an ai risk assessment and treatment plan using a scoring matrix aligned with iso/iec 23894, carry out an ai system impact assessment and record its findings for decision makers, and draft a statement of applicability that justifies the inclusion or exclusion of iso/iec 42001 annex a controls. The full breakdown of topics is covered session by session in the Course Outline tab above.
The course is built for hands-on practitioners who will produce or review governance records, such as: Risk officers and compliance analysts asked to review AI use cases, Data protection officers and privacy analysts, Data analysts, data scientists and machine learning engineers, IT auditors and internal audit staff assessing AI controls, Product managers and business analysts introducing AI features, Information security analysts and ISO/IEC 27001 implementers, Quality and documentation officers supporting certification projects, ICT and digital officers in ministries, local authorities and public agencies, and Monitoring, evaluation and data staff in NGOs using predictive or generative tools.
Practical AI Governance, Responsible AI & ISO 42001 Training Course typically runs as 10 Days. It's available as in-person classroom, live virtual, and in-house corporate training — every course can also be delivered on-site for your team on dates that suit you.
Practical AI Governance, Responsible AI & ISO 42001 Training Course is scheduled in-classroom in Nairobi, Kenya, Mombasa, Kenya, Naivasha, Kenya, and Kisumu, Kenya, and 14 other locations, plus a live interactive virtual classroom you can join from anywhere. Check the schedule panel above for exact upcoming dates and fees in each location.
The next live virtual cohort of Practical AI Governance, Responsible AI & ISO 42001 starts November 16, 2026, with new classroom cohorts also running on a rolling basis. Pick a date and location in the schedule panel above, then click "Register for the Course" — it takes a few minutes and your seat is confirmed once payment or a signed purchase order is received.
Yes — delegates who meet the attendance requirement receive a Certificate of Completion for Practical AI Governance, Responsible AI & ISO 42001 Training Course from Vision Reach Global Consultancy, issued in the name you register with, so double-check the spelling at checkout.
Practical AI Governance, Responsible AI & ISO 42001 Training Course is pitched at intermediate professionals. If you're unsure whether it's the right fit for your current role or background, message our training advisors before you register and they'll help you confirm.
Fees for Practical AI Governance, Responsible AI & ISO 42001 Training Course vary by delivery location and format and are shown in real time in the schedule panel above once you pick a date. Register 3 or more delegates on the same course together and a 5% team discount is applied automatically — larger cohorts can request a custom corporate quote.
Yes — Practical AI Governance, Responsible AI & ISO 42001 Training Course can be delivered on-site at your offices (or virtually for distributed teams), with case studies and examples tailored to your industry and the specific challenges your team is working through. Switch to the "In-House" tab in the schedule panel above to request a proposal.
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