ISO 42001 Training

Lesson 1 • What Is ISO 42001

Introduces the standard's origin, structure, and intended audience. Connects AI-specific governance needs to the broader management system framework.

Lesson 2 • AI Systems and Their Characteristics

Defines AI systems as addressed by the standard and distinguishes them from conventional software. Grounds later risk and control discussions in technical reality.

Lesson 3 • High-Level Structure of the Standard

Maps the standard's clause structure using the harmonised high-level framework. Prepares learners to navigate the full document confidently.

Lesson 1 • AI Policy Development

Teaches how to draft, approve, and communicate an AI policy aligned with organisational objectives. A strong policy anchors all downstream controls.

Lesson 2 • Understanding Organisational Context

Covers internal and external factors that shape the AI management system's design. Learners apply context analysis tools to real organisational scenarios.

Lesson 3 • Stakeholder Engagement and Communication

Addresses how to identify, prioritise, and engage stakeholders throughout the AI management system lifecycle. Effective engagement reduces resistance and builds trust.

Lesson 4 • Defining the AIMS Scope

Guides learners through scoping decisions that determine which AI activities fall under the management system. Accurate scoping prevents gaps and over-engineering.

Lesson 1 • Risk Treatment Options

Explains the four treatment strategies and how to select appropriate controls from the standard's annex. Learners draft a risk treatment plan for a defined scenario.

Lesson 2 • AI Risk Concepts and Taxonomy

Introduces risk terminology and categories unique to AI systems, including bias, opacity, and unintended behaviour. Provides a shared vocabulary for the assessment process.

Lesson 3 • Maintaining the Risk Register

Establishes practices for keeping the risk register current as AI systems and environments evolve. Links risk register updates to management review and audit cycles.

Lesson 4 • Risk Assessment Process

Walks through the standard's required steps for assessing AI risks systematically. Learners apply criteria for likelihood and consequence to realistic scenarios.

Lesson 1 • Documented Information Requirements

Specifies which documents and records the standard mandates and how to manage them. Proper documentation supports both operational control and audit readiness.

Lesson 2 • Annex A Controls Overview

Surveys the control categories in the standard's annex and their intended outcomes. Learners map controls to risk scenarios to build selection intuition.

Lesson 3 • Setting AI Management Objectives

Defines criteria for objectives that are specific, measurable, and aligned with the AI policy. Clear objectives drive accountability and enable performance tracking.

Lesson 4 • Statement of Applicability

Guides learners through selecting, justifying, and documenting applicable controls. The statement of applicability is a central audit artifact.

Lesson 1 • Deployment and Change Management

Addresses controls for releasing AI systems into production and managing subsequent changes. Structured change management prevents unintended degradation of system behaviour.

Lesson 2 • Model Development and Validation

Establishes controls for model selection, training, testing, and validation against defined criteria. Rigorous validation reduces the risk of deploying underperforming models.

Lesson 3 • Third-Party and Supply Chain Controls

Manages risks from external AI providers, datasets, and components integrated into the system. Supply chain controls extend governance beyond organisational boundaries.

Lesson 4 • Data Governance and Quality Controls

Covers controls for data sourcing, labelling, quality assurance, and lineage tracking. Data quality directly determines AI system reliability and fairness outcomes.

Lesson 5 • Responsible AI Design Principles

Embeds fairness, transparency, and accountability into AI system design from the outset. Early design controls reduce costly remediation later in the lifecycle.

Lesson 1 • Management Review Process

Structures the management review meeting to evaluate system performance and drive decisions. Reviews translate data into strategic adjustments and resource commitments.

Lesson 2 • AI System Performance Metrics

Covers technical and ethical metrics for evaluating AI model behaviour in production. Metrics must reflect both accuracy and responsible AI criteria.

Lesson 3 • Continuous Monitoring of AI Outputs

Implements ongoing monitoring of deployed AI systems to detect drift, degradation, and emerging risks. Continuous monitoring closes the loop between deployment and governance.

Lesson 4 • Monitoring and Measurement Framework

Establishes what to measure, how to measure it, and at what frequency for both the system and its AI outputs. A structured framework ensures consistent, comparable data.

Lesson 1 • Identifying Nonconformities

Defines what constitutes a nonconformity in an AI management system context and how to document it. Accurate identification is the prerequisite for effective corrective action.

Lesson 2 • Root Cause Analysis Techniques

Applies structured root cause analysis methods to AI management system failures. Identifying true causes prevents recurrence rather than treating symptoms.

Lesson 3 • Corrective Action Planning

Translates root cause findings into targeted corrective actions with owners and deadlines. Well-structured plans ensure accountability and measurable closure.

Lesson 1 • Evidence Collection and Documentation

Identifies the evidence auditors expect and how to organise it for efficient review. Well-organised evidence packages reduce audit duration and finding risk.

Lesson 2 • Sustaining Certification Long-Term

Establishes practices that maintain conformance between surveillance visits and across recertification cycles. Sustained conformance requires embedding governance into daily operations.

Lesson 3 • Managing Audit Findings

Prepares learners to respond professionally to major and minor nonconformities and observations. Effective responses demonstrate organisational maturity to the certification body.

Lesson 4 • Pre-Audit Gap Analysis

Guides learners through a structured gap analysis against all standard requirements. Gap analysis outputs prioritise remediation efforts before the certification audit.