Training Module
AI System Lifecycle & Inventory
Define AI system scope, set lifecycle boundaries, and maintain an AI system inventory aligned with ISO/IEC 42001
Overview
When AI capabilities are embedded across products and services, boundaries blur and inventories become unreliable. Without clear definitions, governance weakens and assurance findings follow.
This module establishes a structured approach to defining AI system boundaries, distinguishing management system scope from individual system scope, and setting meaningful lifecycle checkpoints. Participants learn how to design and maintain a traceable AI system inventory with clear ownership, defined metadata, and embedded update routines. The focus is on creating defensible traceability from system definition to risk, controls, and monitoring activities.
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
What ISO/IEC 42001 needs from “scope” at the AI system level
Defining “AI system” consistently for inventory purposes
Lifecycle boundaries that matter for governance and assurance
Designing the AI system inventory
Inventory governance and maintenance routines
Using the inventory as a backbone for audit-ready implementation
Case-based workshop
Show detailed agenda...
Learning outcomes
Key outcomes
Apply criteria to identify AI systems and describe their purpose and operating context
Define lifecycle boundaries for AI systems, including development, deployment, maintenance and retirement
Build a maintained inventory of AI systems with minimum metadata and ownership assignments
Additional capabilities
Distinguish between AIMS scope definition and AI system identification to avoid conflation
Design ownership, change and maintenance routines for the AI system inventory
Create traceability links from the inventory to risk assessments, impact/harm evaluations and operational control activities
Materials
Learning materials
Slide deck
Participant workbook
Templates & tools
Practical, reusable artefacts to apply the module directly to your organisation.
AI system inventory management process
AI system identification checklist
AI system inventory register
Lifecycle boundary and accountability canvas
AI prompt set for AI system inventory management
Confirmation
Certificate of completion
Module ID
HAM-AI-S-01
Discipline
ISO standard
Standard clause
4: Context of the organisation
Domains
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 general familiarity with management system implementation concepts and documented information discipline. Participants should also be comfortable with basic AI lifecycle terminology at a conceptual level (no technical depth required).
Helpful background includes:
Understanding of management system scope concepts and boundary thinking
Familiarity with registers, ownership, and change control as governance tools
Basic awareness of how AI-enabled capabilities are developed or sourced (build/buy/embedded)
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.
