Training Module
Operational Control of AI Systems
Define, implement and maintain operational controls for AI systems across deployment, change and monitoring
Overview
Selecting controls for AI systems is only the starting point. The real challenge is embedding them into daily build, deployment, monitoring, and change practices so they remain effective as systems evolve.
This module provides a structured approach to translating ISO/IEC 42001 operational control expectations into lifecycle control points. Participants learn how to design proportionate control sets based on system impact and risk, define clear roles and escalation paths, and establish release and change routines that preserve control integrity. The focus is on demonstrable effectiveness: identifying the minimum evidence required and preventing “paper controls” that exist in documentation but not in practice.
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
Operational control in an ISO/IEC 42001 AI management system
Defining operational control requirements per AI system and use context
Lifecycle control points: build, deploy, use, and monitor
Change and release discipline for AI systems
Operational roles, responsibilities, and competence in AI control
Documented information and evidence for operational control
Case-based workshop
Show detailed agenda...
Learning outcomes
Key outcomes
Translate ISO/IEC 42001 operational control expectations into lifecycle control points for AI systems
Define control sets that are proportionate to the AI system’s impact and risk and integrate them into build, deployment and use
Establish change and release practices that ensure controls remain effective as systems evolve
Additional capabilities
Specify roles, responsibilities and escalation paths for AI operational control
Identify the minimum evidence needed to demonstrate that controls are working
Recognise paper controls and design corrective actions to embed controls into daily routines
Materials
Learning materials
Slide deck
Participant workbook
Templates & tools
Practical, reusable artefacts to apply the module directly to your organisation.
AI operational control pack template
Lifecycle control points map
AI change & release checklist template
Re-approval trigger log template
Operational monitoring review log template
Exception & escalation record template
Supporting AI prompt set
Confirmation
Certificate of completion
Module ID
HAM-AI-S-03
Discipline
ISO standard
Standard clause
8: Operation
Target audience
Public delivery
Live virtual
Duration
7 h
List price
CHF 550
Excl. VAT. VAT may apply depending on customer location and status.
Delivery
Live virtual delivery
This module is delivered live online and combines conceptual framing, discussion, case work and direct interaction with the instructor.
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.
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.
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
This module assumes participants can work within a management system and interpret operational roles and evidence requirements.
Helpful background includes:
Basic understanding of management system roles, procedures, and documented information
Familiarity with how risk decisions and approvals shape operational constraints
Practical understanding of AI lifecycle artefacts and monitoring
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.
