Certified Lead Auditor ISO 42001 Artificial Intelligence AI

1. Master key concepts and foundational definitions of the standard ISO 42001.

2. Understand the terminology required for understand management systems

Essential Concepts of ISO/IEC 22989

In this first learning block, we will delve into the fundamental concepts that govern the world of artificial intelligence (AI) from a regulatory perspective. Our students will learn what AI is and is not, how it differs from other automated technologies, and why it is essential to establish a common language and clear criteria before auditing, designing, or implementing AI-based systems.

We will explore the following key concepts defined by ISO/IEC 22989:

Artificial Intelligence vs. Deterministic Systems

AI Systems and Key Components

Machine Learning

Trained vs. Untrained Models

Training, Validation, and Test Data

Biases, Uncertainty, and Errors in AI

Explainability, Traceability, Autonomy, and Adaptability

General Artificial Intelligence vs. Weak AI

Hybrid and Supervised Systems

Intelligent Agents, Contexts, Environments, and Goals

Additionally, students will become familiar with practical understanding tools, including:

Conceptual diagrams to visualize the relationships between normative terms.

Real-life use cases, such as virtual assistants, autonomous vehicles, or predictive models, to apply the concepts.

Interactive glossaries, which facilitate mastery of the technical vocabulary required by the standard.

Initial activities focused on classifying and auditing technologies according to the criteria of the ISO/IEC 22989 standard.

This block provides the theoretical and conceptual foundation that will allow students to confidently navigate the subsequent levels of the course, where we will address risks, impact assessment, AI governance, and the requirements established by ISO/IEC 42001.

In this first project we will focus on TensorFlow (Keras) because it offers a very gentle learning curve inside Colab: just a few lines are enough to define and train the network, and TensorBoard is already integrated for auditing. The goal is to learn how to verify, explain, and document the performance of a Feed‑Forward Neural Network (FFNN). Later, in a second exercise, we will reproduce part of GPT‑2 with PyTorch to contrast approaches and repeat the audit in a generative‑model context, but the conceptual foundation is laid here.

Tools and concepts you will master

• Google Colab – Cloud notebook with free GPU: run everything without installing a thing.

• TensorFlow / Keras – High‑level framework for building and training the FFNN.

• TensorBoard – Interactive dashboard to inspect loss/accuracy curves, weight histograms, and the computational graph.

• Feed‑Forward Neural Network (FFNN) – Basic AI architecture for binary classification.

• AI audit process – Input‑data validation, over‑fitting detection, architecture review, and metric traceability (aligned with ISO 22989 / ISO 42001).

• Model governance – Pin library versions, log every run, and translate technical findings into business decisions.

• PyTorch + GPT‑2 (later phase) – Will be used to simulate a language model and repeat the audit, comparing results with the TensorFlow‑based workflow.

With this progressive path, students will gain a comprehensive view of how to audit neural networks—first in a classic supervised setting and then in a generative‑AI scenario.

In this block of the course, students will continue to develop a structured and up-to-date understanding of the technical, functional, and methodological concepts that define artificial intelligence according to ISO/IEC 22989.

Key concepts students will master:

• Data mining vs. machine learning

• Machine learning algorithms and their training

• Autonomous and adaptive decision-making

• Lifelong learning

• Explainable models and algorithmic traceability

• Differences between symbolic and subsymbolic AI

• Artificial neural networks (FFNN, CNN, RNN, LSTM)

• Data quality evaluation and validation

• Big data

• Semantic computing and ontological structures

• IoT, cyber-physical systems, and AI infrastructure (cloud and edge computing)

• Functional life cycle of AI systems

• AI ecosystems, layered architectures, and functional components in intelligent systems

  
  

Learn the main fields of application of AI and how to understand how it works:

Main application domains of AI:

• Computer vision (We will do a practical exercise to understand it)

• Natural language processing (NLP)

• Natural Language Generation (NLG)

• Conversational agents

• Optical Character Recognition (OCR)

• Text-to-Image/Video Generation (T2I)

• Recommendation and personalization systems

• Applications in healthcare, education, industry, finance, and transportation

Learn to use good tools like ChatGPT, Eleven Labs, Heygen, Sora, Explotion, so on.

1. Chapter 4.1: Analyze your organization's context for AI implementation.

2. Chapter 4.2: Identify stakeholders and their expectations.

3. Chapter 4.3: Define the scope of the AI management system.

4. Chapter 4.4: Design an AI system aligned with strategic goals.

5. Chapter 5.1: Evaluate leadership and commitment to ethical AI.

6. Chapter 5.2: Create an organizational AI policy.

7. Chapter 5.3: Assign clear roles and responsibilities.

8. Chapter 6.2: Set measurable objectives and plan how to achieve them.

You will learn to:

• Integrate all artefacts into a single case (e.g., “Dropout Risk v2” or an EdTech pilot).

• Demonstrate end-to-end deployment: context → policy → scope → stakeholders → objectives → plan → controls → roles → evidence.

• Map PII with ISO 29100 roles (principal, controller, processor, third party) and data flows; align Annex A safeguards.

• Build a traceability matrix so an auditor can follow the red thread from an issue/expectation to the implemented control and its evidence.

Exercise deliverables:

• Context Register (prioritised), Stakeholder Register (management & metrics), Scope Statement, AI Policy (operational excerpt), Process Map + RACI, Roles & Authorities Register, PII map + flows, Objectives Register + Plans, Traceability Matrix, and a KPI dashboard mockup (6.2 monitoring).

Mastery criteria:

• Cross-artefact coherence (no contradictions).

• Objectives with resources, owners and evaluation methods that are verifiable.

• Linked evidence (minutes, contracts, technical controls) sufficient for audit.

1. By the end of this module (ISO/IEC 42001 clauses 6.1, 8.2, 8.3, 8.4 and Annex B), learners will be able to:

   • Plan risk & opportunity management for AI: set risk criteria, plan actions, integrate them into processes, and evaluate effectiveness. Norma ISO 42001 Norma ISO 42001 Norma ISO 42001

   • Run AI risk assessments aligned with AI policy and objectives; ensure consistent, valid, comparable results; identify risks to objectives; analyze consequences, likelihood and risk levels; and keep records. Norma ISO 42001 Norma ISO 42001

   • Treat risks by selecting options, determining controls and mapping them to Annex A with Annex B guidance; issue a Statement of Applicability (SoA) with justifications; execute the plan with management approval of residual risk. Norma ISO 42001 Norma ISO 42001 Norma ISO 42001

   • Operate & maintain assessments and treatments: perform risk and impact assessments periodically or on significant changes; verify treatment effectiveness and update plans; retain documented information. Norma ISO 42001 Norma ISO 42001 Norma ISO 42001

   • Conduct AI system Impact Assessments (EIA) to identify consequences on individuals, groups and society across the lifecycle; document outcomes and feed risk assessment. Norma ISO 42001

   • Use Annex B as practical implementation guidance; its content need not be justified in the SoA.

In this module (Annex A & B) you’ll learn how to manage third-party and customer relationships across the AI life cycle: how to allocate responsibilities using a contractual RACI, run supplier due-diligence (data, bias, security, explainability), and align customer expectations (intended use, transparency, support, complaint channels). You’ll practice drafting key clauses (allowed/forbidden use, incident reporting, retention, audits), assembling evidence packs (system/model cards, AI impact assessments, subgroup metrics), and using KPIs and joint runbooks for monitoring, escalation, and kill-switch. You’ll leave with templates and prompts to implement these practices with traceability and auditability.

Welcome to the "Support" section of our ISO 42001 AI Management System Auditor Certification course on Udemy. This module is designed to provide you with comprehensive insights into the foundational elements that support the effective implementation and maintenance of an AI Management System (AIMS).

1. Overview:

   • Resources: Understand the importance of allocating appropriate resources for the AIMS, ensuring its success and sustainability.

   • Competence: Dive deep into the skills, knowledge, and training required for personnel involved in significant energy use areas.

   • Awareness: Learn about the significance of creating awareness among all members of the organization regarding the AIMS, its benefits, and their individual roles in its success.

   • Communication: Explore the various internal and external communication strategies and protocols that ensure smooth operation and stakeholder engagement.

   • Documented Information: Grasp the importance of maintaining and retaining documented information, ensuring traceability, and easy access for audits and reviews.

   By the end of this module, you'll have a solid understanding of the support mechanisms necessary for the effective functioning of an AIMS. This knowledge will be crucial as you move forward in your journey to becoming a certified ISO 42001 AI Management System Auditor.

What you’ll learn in Clauses 8.1, 8.3, 8.4 (ISO/IEC 42001):

8.1 Operational planning and control

You will be able to:

• Map operational criteria to verifiable controls (procedures, roles, use limits, records) and test design vs. operating effectiveness using objective evidence.

• Trace end-to-end “policy → procedure → record → outcome” across critical AI activities (data governance, change, third parties, incidents, retirement).

• Apply benchmarking against industry practices (e.g., Top controls for most use LLM like Google, OpenAI, Amazon, Microsoft) to form a professional opinion on control sufficiency.

• Spot typical nonconformities (defined but unevidenced controls, vague criteria, unclear ownership) and strengths (measurable criteria, complete evidence, duty segregation).

• Change management and suppliers control.

8.3 AI risk treatment

You will be able to:

• Assess treatment choices (mitigate/transfer/accept/avoid) and their consistency with prior risk assessment (8.2) and selected controls.

• Verify evidence: risk→control matrix, acceptance criteria, owners, timelines, and proof of implementation; alignment with Annexes A/B.

• Detect misalignments (high risks with weak controls, unjustified risk acceptance) and document improvements (harden controls, adjust thresholds, strengthen suppliers).

8.4 AI system impact assessment (AIA)

You will be able to:

• Review AIA methods: triggers, coverage (security, privacy, bias, legal/ethics, safety), stakeholders, and how decisions are evidenced.

• Check integration of AIA outcomes into requirements and controls (e.g., use-case limits, HIL for high-impact actions, data restrictions, user disclosures).

• Judge documentation quality (scope, assumptions, alternatives, mitigations, re-assessments) and triggers for re-doing AIA (new data/model, context changes).

  
  

You’ll deliver defensible conclusions on: (i) operational control sufficiency; (ii) consistency between risks and treatments; and (iii) robustness of AIAs and their traceability to controls and evidence—without operating the platforms.

1. Chapter 9.1: Define KPIs to measure SGIA performance.

2. Chapter 9.2: Conduct internal audits.

3. Chapter 9.3: Carry out management reviews.

Learn to structure corrective actions as a professional.