ISO 42001 Artificial Intelligence Management Systems Training Coursee Live Virtual Tutor Led (PECB)

About Course

The ISO/IEC 42001 Artificial Intelligence Management System (AIMS) Training Course – PECB Certified is an advanced live virtual tutor-led programme designed to equip participants with the knowledge and competencies required to implement, manage, audit, and continually improve Artificial Intelligence Management Systems in accordance with ISO/IEC 42001:2023.

This course provides a structured understanding of AI governance, risk management, ethical AI principles, and compliance requirements for organizations that design, develop, deploy, or use artificial intelligence systems. It focuses on ensuring that AI systems are transparent, accountable, secure, and aligned with organizational and regulatory expectations.

Through interactive virtual sessions, case studies, and practical exercises, participants gain the skills needed to establish responsible AI governance frameworks and support ISO/IEC 42001 certification readiness.

The course follows the PECB certification pathway and prepares participants for the ISO/IEC 42001 Lead Implementer / Lead Auditor (as applicable) certification examination.

Understand ISO/IEC 42001 requirements and AI governance principles.
Implement an Artificial Intelligence Management System (AIMS).
Identify and manage AI risks including bias, privacy, and security concerns.
Ensure ethical and responsible AI development and deployment.
Integrate AI governance into organizational management systems.
Monitor and evaluate AI system performance.
Align AI systems with legal and regulatory requirements.
Support ISO/IEC 42001 certification readiness.
Apply best practices in AI risk and compliance management.
Build competence in responsible AI leadership.

Course Content

Introduction to Artificial Intelligence and ISO 42001
Overview of artificial intelligence systems Evolution of AI governance and regulation Introduction to ISO/IEC 42001:2023 Importance of AI management systems

ISO 42001 Framework and Structure
Scope and clauses of ISO 42001 Plan-Do-Check-Act (PDCA) model for AI systems Integration with other management systems (ISO 9001, ISO 27001, etc.) Organizational context for AI systems

AI Governance and Leadership
Leadership roles and responsibilities AI policy development and implementation Accountability and oversight mechanisms Organizational culture for responsible AI

AI Risk Management
Identification of AI-related risks Bias, fairness, and discrimination risks Privacy, security, and safety risks Risk assessment and treatment approaches

Data Management in AI Systems
Data governance principles Data quality and lifecycle management Data bias and integrity issues Ethical data usage in AI systems

AI System Lifecycle Management
AI system design and development Deployment, monitoring, and maintenance Change management in AI systems Decommissioning AI systems responsibly

Ethical and Responsible AI
Ethical AI principles and frameworks Transparency and explainability Human oversight and intervention Bias mitigation strategies

Performance Evaluation and Improvement
AI performance metrics and KPIs Monitoring AI system behavior Internal audit and evaluation processes Continual improvement of AI systems

Compliance and Legal Requirements
AI regulatory landscape and emerging laws Compliance obligations for organizations Documentation and evidence requirements Conformity assessment processes

Implementation and Certification Preparation
Implementing ISO/IEC 42001 in organizations Common implementation challenges Best practices and case studies Preparation for PECB certification examination

Student Ratings & Reviews

No Review Yet

ISO 42001 Artificial Intelligence Management Systems Training Coursee Live Virtual Tutor Led (PECB)

About Course

What Will You Learn?

Course Content

Introduction to Artificial Intelligence and ISO 42001
Overview of artificial intelligence systems Evolution of AI governance and regulation Introduction to ISO/IEC 42001:2023 Importance of AI management systems

ISO 42001 Framework and Structure
Scope and clauses of ISO 42001 Plan-Do-Check-Act (PDCA) model for AI systems Integration with other management systems (ISO 9001, ISO 27001, etc.) Organizational context for AI systems

AI Governance and Leadership
Leadership roles and responsibilities AI policy development and implementation Accountability and oversight mechanisms Organizational culture for responsible AI

AI Risk Management
Identification of AI-related risks Bias, fairness, and discrimination risks Privacy, security, and safety risks Risk assessment and treatment approaches

Data Management in AI Systems
Data governance principles Data quality and lifecycle management Data bias and integrity issues Ethical data usage in AI systems

AI System Lifecycle Management
AI system design and development Deployment, monitoring, and maintenance Change management in AI systems Decommissioning AI systems responsibly

Ethical and Responsible AI
Ethical AI principles and frameworks Transparency and explainability Human oversight and intervention Bias mitigation strategies

Performance Evaluation and Improvement
AI performance metrics and KPIs Monitoring AI system behavior Internal audit and evaluation processes Continual improvement of AI systems

Compliance and Legal Requirements
AI regulatory landscape and emerging laws Compliance obligations for organizations Documentation and evidence requirements Conformity assessment processes

Implementation and Certification Preparation
Implementing ISO/IEC 42001 in organizations Common implementation challenges Best practices and case studies Preparation for PECB certification examination

Our Partners and clients

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Training Services

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About Course

What Will You Learn?

Course Content

Introduction to Artificial Intelligence and ISO 42001 Overview of artificial intelligence systems Evolution of AI governance and regulation Introduction to ISO/IEC 42001:2023 Importance of AI management systems

ISO 42001 Framework and Structure Scope and clauses of ISO 42001 Plan-Do-Check-Act (PDCA) model for AI systems Integration with other management systems (ISO 9001, ISO 27001, etc.) Organizational context for AI systems

AI Governance and Leadership Leadership roles and responsibilities AI policy development and implementation Accountability and oversight mechanisms Organizational culture for responsible AI

AI Risk Management Identification of AI-related risks Bias, fairness, and discrimination risks Privacy, security, and safety risks Risk assessment and treatment approaches

Data Management in AI Systems Data governance principles Data quality and lifecycle management Data bias and integrity issues Ethical data usage in AI systems

AI System Lifecycle Management AI system design and development Deployment, monitoring, and maintenance Change management in AI systems Decommissioning AI systems responsibly

Ethical and Responsible AI Ethical AI principles and frameworks Transparency and explainability Human oversight and intervention Bias mitigation strategies

Performance Evaluation and Improvement AI performance metrics and KPIs Monitoring AI system behavior Internal audit and evaluation processes Continual improvement of AI systems

Compliance and Legal Requirements AI regulatory landscape and emerging laws Compliance obligations for organizations Documentation and evidence requirements Conformity assessment processes

Implementation and Certification Preparation Implementing ISO/IEC 42001 in organizations Common implementation challenges Best practices and case studies Preparation for PECB certification examination

Student Ratings & Reviews

Our Partners and clients

Quick Links

Training Services

Subscribe

Introduction to Artificial Intelligence and ISO 42001
Overview of artificial intelligence systems Evolution of AI governance and regulation Introduction to ISO/IEC 42001:2023 Importance of AI management systems

ISO 42001 Framework and Structure
Scope and clauses of ISO 42001 Plan-Do-Check-Act (PDCA) model for AI systems Integration with other management systems (ISO 9001, ISO 27001, etc.) Organizational context for AI systems

AI Governance and Leadership
Leadership roles and responsibilities AI policy development and implementation Accountability and oversight mechanisms Organizational culture for responsible AI

AI Risk Management
Identification of AI-related risks Bias, fairness, and discrimination risks Privacy, security, and safety risks Risk assessment and treatment approaches

Data Management in AI Systems
Data governance principles Data quality and lifecycle management Data bias and integrity issues Ethical data usage in AI systems

AI System Lifecycle Management
AI system design and development Deployment, monitoring, and maintenance Change management in AI systems Decommissioning AI systems responsibly

Ethical and Responsible AI
Ethical AI principles and frameworks Transparency and explainability Human oversight and intervention Bias mitigation strategies

Performance Evaluation and Improvement
AI performance metrics and KPIs Monitoring AI system behavior Internal audit and evaluation processes Continual improvement of AI systems

Compliance and Legal Requirements
AI regulatory landscape and emerging laws Compliance obligations for organizations Documentation and evidence requirements Conformity assessment processes

Implementation and Certification Preparation
Implementing ISO/IEC 42001 in organizations Common implementation challenges Best practices and case studies Preparation for PECB certification examination