Curriculum
- 2 Sections
- 36 Lessons
- 26 Weeks
- ISO 4200111
- 1.1Introduction to ISO/IEC 42001:2023 – Artificial Intelligence Management Systems
- 1.2Scope and Applicability of ISO/IEC 42001:2023
- 1.3Leadership and Organizational Commitment in ISO/IEC 42001:2023
- 1.4AI Lifecycle Governance in ISO/IEC 42001:2023
- 1.5Risk Management in ISO/IEC 42001:2023
- 1.6Data and AI Model Management in ISO/IEC 42001:2023
- 1.7Monitoring and Performance Evaluation in ISO/IEC 42001:2023
- 1.8Transparency, Accountability, and Documentation in ISO/IEC 42001:2023
- 1.9Continuous Improvement in ISO/IEC 42001:2023
- 1.10Integration with Other Management Standards in ISO/IEC 42001:2023
- 1.11Compliance with Ethical and Legal Requirements in ISO/IEC 42001:2023
- ISO 19011: Guidelines for auditing management systems26
- 2.1Introduction to ISO19011
- 2.2Principles of Auditing
- 2.3Managing an Audit Program
- 2.4Establishing Audit Program Objectives
- 2.5Determining Audit Program Risks and Opportunities
- 2.6Establishing the Audit Program
- 2.7Implementing the Audit Program
- 2.8Monitoring the Audit Program
- 2.9Reviewing and Improving the Audit Program
- 2.10Initiating the Audit
- 2.11Determining Audit Feasibility
- 2.12Preparing Audit Activities
- 2.13Reviewing Documented Information
- 2.14Preparing the Audit Plan
- 2.15Assigning Work to the Audit Team
- 2.16Preparing Working Documents
- 2.17Opening Meeting
- 2.18Communication During the Audit
- 2.19Collecting and Verifying Information
- 2.20Generating Audit Findings
- 2.21Preparing Audit Conclusions
- 2.22Closing Meeting
- 2.23Preparing the Audit Report
- 2.24Completing the Audit
- 2.25Follow-Up Activities
- 2.26ISO 42001 Exam120 Minutes40 Questions
AI Lifecycle Governance in ISO/IEC 42001:2023
AI Lifecycle Governance in ISO/IEC 42001:2023
Data management is a critical component of the AI lifecycle. ISO 42001 requires organizations to establish procedures for data collection, processing, storage, and usage that comply with legal and regulatory requirements. Data quality, integrity, privacy, and security must be ensured throughout the lifecycle. Proper governance of datasets includes procedures for handling sensitive information, managing biases, and maintaining transparency in data sourcing and usage. AI system outputs must be monitored for accuracy and fairness, and documentation of data provenance is necessary to support ethical and accountable AI practices.
Deployment and integration of AI systems require careful governance to ensure alignment with organizational processes, controls, and policies. ISO 42001 mandates that organizations establish mechanisms to monitor AI system performance, detect anomalies, and manage operational risks. Governance during deployment includes establishing monitoring protocols, incident reporting structures, and escalation procedures for deviations from expected outcomes. Continuous oversight ensures that AI systems operate safely, ethically, and in compliance with established policies and regulatory requirements.