Artificial Intelligence Management System
As artificial intelligence reshapes industries worldwide, learners with expertise in AI governance and auditing are in high demand. The LICQual ISO/IEC 42001:2023 – Artificial Intelligence Management System (AIMS) Lead Auditor course is a globally recognized program designed to equip learners with the skills to audit, assess, and certify AI systems in line with ISO/IEC 42001:2023 standards.
This course provides learners with in-depth knowledge of AI management systems, focusing on risk management, compliance, data governance, and responsible AI practices. Learners gain practical skills to lead audits that ensure ethical, transparent, and secure deployment of AI technologies, integrating international auditing principles with AI-specific technical guidance.
Throughout the program, learners explore AI lifecycle stages, establish AI policies, verify algorithmic ethics, and assess conformity to organizational and regulatory requirements. The course covers audit preparation, planning, execution, reporting, and follow-up, using real-world case studies and scenario-based exercises to reinforce practical application.
Learners also develop leadership capabilities to manage audit teams, conduct risk-based assessments, and report findings in accordance with ISO 19011 guidelines. By completing the LICQual ISO/IEC 42001:2023 Lead Auditor course, learners are fully prepared to perform first, second, and third-party AI audits, ensuring responsible AI governance, building stakeholder trust, and advancing their careers in compliance, risk management, and AI oversight.
Course Overview
Qualification Title
LICQual ISO/IEC 42001:2023 – Artificial Intelligence Management System (AIMS) Lead Auditor
Total Units
6
Total Credits
40
GLH
120
Qualification #
LICQ2200427
Qualification Specification
To enroll in the LICQual ISO/IEC 42001:2023 – Artificial Intelligence Management System (AIMS) Lead Auditor applicants must meet the following criteria:
|
Qualification# |
Unit Title |
Credits |
GLH |
|---|---|---|---|
|
LICQ2200427-1 |
Understanding ISO/IEC 42001:2023 and the AIMS Framework |
8 |
24 |
|
LICQ2200427-2 |
AI Risk Management and Ethical Governance |
8 |
24 |
|
LICQ2200427-3 |
Planning and Preparing for an AIMS Audit |
6 |
18 |
|
LICQ2200427-4 |
Conducting AIMS Audits: Techniques and Tools |
6 |
18 |
|
LICQ2200427-5 |
Reporting, Nonconformities, and Follow-up Actions |
6 |
18 |
|
LICQ2200427-6 |
Global Legal, Regulatory, and Data Protection Considerations in AI |
6 |
18 |
By the end of this course, learners will be able to:
1. Understanding ISO/IEC 42001:2023 and the AIMS Framework
- Explain the purpose, structure, and scope of ISO/IEC 42001:2023.
- Interpret key terminology, clauses, and requirements of an Artificial Intelligence Management System.
- Describe how AIMS integrates with organizational strategy, AI lifecycle, and quality systems.
2. AI Risk Management and Ethical Governance
- Identify and assess AI-specific risks including bias, misuse, and unintended outcomes.
- Apply risk mitigation techniques aligned with ethical and responsible AI principles.
- Evaluate governance structures that ensure transparency, accountability, and trust in AI systems.
3. Planning and Preparing for an AIMS Audit
- Develop audit plans in line with ISO 19011 auditing guidelines.
- Define audit scope, criteria, and objectives for Artificial Intelligence Management Systems.
- Prepare necessary documentation and tools for effective audit execution.
4. Conducting AIMS Audits: Techniques and Tools
- Perform systematic audits using interviews, checklists, and document reviews.
- Apply appropriate audit techniques to evaluate compliance and effectiveness of AI controls.
- Assess audit findings against ISO/IEC 42001:2023 and organizational requirements.
5. Reporting, Nonconformities, and Follow-up Actions
- Create clear and professional audit reports identifying strengths, weaknesses, and nonconformities.
- Recommend actionable corrective measures based on audit results.
- Conduct follow-up activities to verify resolution of nonconformities and drive continual improvement.
6. Global Legal, Regulatory, and Data Protection Considerations in AI
- Interpret key global legal and regulatory requirements related to AI systems.
- Assess organizational compliance with data privacy laws such as GDPR and emerging AI legislation.
- Incorporate legal and ethical considerations into audit findings and recommendations.
This diploma is ideal for:
- Professionals responsible for auditing or assessing AI management systems
- Compliance officers working with emerging AI standards and regulations
- IT and data governance managers overseeing responsible AI implementation
- Lead auditors certified in other ISO standards seeking AI specialization
- Risk managers handling AI-related compliance and ethical challenges
- AI developers and system architects aiming to understand audit processes
- Cybersecurity professionals monitoring AI system integrity and threats
- Quality assurance personnel involved in AI lifecycle governance
- Legal and regulatory advisors focused on AI accountability frameworks
- Internal auditors transitioning into AI-specific auditing roles
- Consultants providing AI governance, risk, and compliance services
- Technical managers responsible for enterprise-level AI deployment
- Ethics and trust officers ensuring fairness and transparency in AI
- AI project leads seeking alignment with ISO/IEC 42001:2023 standards
- Professionals preparing organizations for AI certification audits
- Data protection officers concerned with AI and GDPR/AI Act compliance
- Policy makers or analysts working with AI governance frameworks
- Individuals aiming to build careers in AI risk and compliance auditing
- Managers overseeing AI implementation in highly regulated industries
- Trainers and educators seeking to deliver accredited AI audit programs
Assessment and Verification
All units within this qualification are subject to internal assessment by the approved centre and external verification by LICQual. The qualification follows a criterion-referenced assessment approach, ensuring that applicants meet all specified learning outcomes.
To achieve a ‘Pass’ in any unit, applicants must provide valid, sufficient, and authentic evidence demonstrating their attainment of all learning outcomes and compliance with the prescribed assessment criteria. The Assessor is responsible for evaluating the evidence and determining whether the applicants has successfully met the required standards.
Assessors must maintain a clear and comprehensive audit trail, documenting the basis for their assessment decisions to ensure transparency, consistency, and compliance with quality assurance requirements.