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
AI inventories often start as lists of tools, pilots, vendor features and use cases. They look complete until a real governance question appears: what exactly is the AI system, where does its boundary sit, who owns the record, and which change should trigger review?
This module teaches AI system lifecycle and inventory work as a practical management-system capability. Participants work through the evolving Northstar case to identify candidate AI systems, separate systems from components and ordinary automation, define boundaries and context of use, record lifecycle state, assign ownership, surface supplier and data dependencies, and maintain traceability to risk, controls, monitoring and assurance. The focus is implementation judgement, not generic AI awareness, legal classification, model development or MLOps.
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
AI system discovery and candidate triage
AI system boundaries and context of use
Lifecycle checkpoints and inventory evidence
Ownership, dependencies and supplier interfaces
Inventory metadata that supports decisions
Change triggers, traceability and assurance limits
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Learning outcomes
Key outcomes
Identify AI systems and unresolved candidates across pilots, embedded vendor features, automation and GenAI use
Define AI system boundaries, context of use and lifecycle state with enough evidence for downstream governance work
Build and maintain an AI inventory record with ownership, dependencies, metadata and update triggers
Additional capabilities
Distinguish between AIMS scope, AI system scope, AI components and AI-enabled work practices
Use lifecycle checkpoints to route proposal, pilot, deployment, change, suspension and retirement decisions
Check traceability from inventory to AI risk assessment, controls, monitoring, management review and assurance claims
Materials
Learning materials
Slide deck
Participant workbook
Templates & tools
Practical, reusable artefacts to apply the module directly to your organisation.
AI system identification decision-tree tool
AI system boundary canvas
Lifecycle checkpoint map
AI inventory register schema and example record
Ownership and dependency matrix
Inventory change trigger and review log
AI-assisted inventory review prompts and safeguards guide
Confirmation
Certificate of completion
Overview
Dates
Bespoke
Module ID
HAM-AI-S-01
Discipline
ISO standard
Standard clause
4: Context of the organisation
Domains
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. Participants should be ready to work with practical management-system concepts such as scope, ownership, registers, documented information and change control. The module introduces the AI system framing and inventory terminology needed for the exercises.
Helpful background includes:
Familiarity with how organisations operate digital services, suppliers and internal tools
Basic awareness that AI capabilities may be built, bought, embedded in vendor tools or used through GenAI services
Comfort discussing roles, responsibilities, evidence and review triggers without needing technical AI depth
Preparatory modules
Supporting modules (optional)
Helpful if you want to deepen related skills, but not required to participate effectively.
