Training Module
Operational Control of AI Systems
Define, implement and maintain operational controls for AI systems across deployment, change and monitoring
Overview
AI governance, inventory, risk assessment and approval decisions only matter when they become part of normal operation. The difficult work is turning those decisions into owned routines, meaningful human oversight, monitored behaviour, controlled change and reviewable evidence.
This module uses a realistic Northstar case to show how operational controls are derived from AI system records, usage conditions, lifecycle state and risk-treatment decisions. Participants practise defining lifecycle control points, assigning responsibilities, handling prompt, data, model and vendor changes, reviewing monitoring signals and preparing evidence that supports management review and customer assurance without overclaiming.
Applicable environments
This module applies to organisations implementing or operating a AI Management System (AIMS) in line with ISO/IEC 42001. It focuses on how the standard’s requirements are interpreted and applied in practice within real organisational contexts.
The content is relevant for organisations seeking certification as well as for those using ISO/IEC 42001 as a reference framework to structure responsibilities, processes, and controls in the AI management domain.
Target audience
People involved in designing, building, operating, or improving an AIMS aligned with ISO/IEC 42001
Executives and department heads accountable for the effectiveness and performance of an AIMS
Those responsible for processes, policies, applications, risks or risk controls related to AI
Auditors of ISO/IEC 42001 who want to deepen their understanding of management-side best practices (not audit technique)
Decision support
Is this module for you?
Agenda
Translate AI decisions into control requirements
Define lifecycle control points and release gates
Assign ownership, oversight and evidence responsibilities
Specify operating routines for use, monitoring and escalation
Handle AI changes, exceptions and re-approval triggers
Review monitoring evidence, feedback and supplier signals
Prepare assurance-ready management-review input
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Learning outcomes
Key outcomes
Translate inventory, risk, approval and lifecycle inputs into AI operational control requirements
Build lifecycle control points and release gates for deployment, use, monitoring, change and retirement
Define operating routines, human oversight and evidence that show controls are working in practice
Additional capabilities
Assign owners, performers, reviewers, evidence responsibilities and escalation authorities for AI controls
Route prompt, data, model, vendor and workflow changes to reassessment, re-approval or control adjustment
Review monitoring evidence, exceptions and weak signals for management review and customer-safe assurance
Materials
Learning materials
Slide deck
Participant workbook
Templates & tools
Practical, reusable artefacts to apply the module directly to your organisation.
AI operational control map
Lifecycle control point and release gate checklist
Control ownership, oversight and interface matrix
AI operational routine specification template
Change, exception and re-approval trigger log
Monitoring evidence review worksheet
AI-assisted control review prompts and safeguards guide
Confirmation
Certificate of completion
Overview
Dates
Bespoke
Module ID
HAM-AI-S-03
Discipline
ISO standard
Standard clause
8: Operation
Target audience
Delivery
Live virtual delivery
This module is delivered live online and combines conceptual framing, discussion, case work and direct interaction with the instructor.
Custom delivery options
For organisations with specific constraints or learning objectives, the module can be adapted in format or scope, including in-house delivery and contextualised case material.
Upcoming course runs
A public cohort is currently not scheduled. If you register your interest, we will notify you when a new public cohort is scheduled or suitable delivery options become available.
For an optimal learning experience
Prerequisites & preparation
This module is designed as part of a modular training approach. Topics are deliberately distributed across modules and are not repeated in full, in order to avoid unnecessary redundancy. Each module is self-contained and can be taken on its own. Where prior knowledge or experience is helpful, this is indicated below so you can decide whether any preparation is useful for you.
Assumed background
No formal prerequisites. The module is designed for participants who can follow basic AI system concepts and are comfortable working with management-system roles, routines, records and evidence.
Participants with very limited AI background should use the preparation recommendations before booking, especially where terms such as AI system boundary, model update, prompt change, human oversight or monitoring trigger are still unfamiliar.
Preparatory modules
Foundational modules (depending on background)
Useful if you are new to the underlying concepts or want a shared baseline before attending this module.
Supporting modules (optional)
Helpful if you want to deepen related skills, but not required to participate effectively.
