Ethics and Social Acceptability of Artificial Intelligence
Description: After completing the course, participants will be able to demonstrate the following competences:
- Recognize and explain ethical challenges in AI systems.
- Interpret societal expectations for responsible AI use.
- Apply ethical frameworks and standards such as ISO/IEC 42001.
- Support inclusive, fair, and human-centered AI system design.
- Contribute to the organizational culture of ethical and socially acceptable AI.
Previous skills/knowledge: Participants are expected to have the following basic knowledge:
- Basic understanding of AI systems and their societal applications.
- Awareness of ethical principles such as fairness, autonomy, and transparency.
- Familiarity with regulatory and public concerns related to emerging technologies.
Authorized Partners:
Teaching requirements: Trainers should meet the following requirements:
- Subject Matter Expertise: in-depth knowledge of AI ethics, societal impact, human rights, and relevant frameworks such as ISO/IEC 42001 and UNESCO recommendations.
- Certifications: preferred qualifications in AI ethics, data protection (e.g., ISO/IEC 27701), or related governance standards.
- Training & Practical Experience: at least 2 years of practical experience in addressing ethical implications of AI or facilitating human-centered design processes.
Objectives to achieve: The course aims to achieve the following objectives:
- Understand ethical risks and responsibilities in AI development and deployment.
- Identify key societal expectations and values related to trustworthy AI.
- Learn about standards, principles, and frameworks guiding ethical AI.
- Gain insight into human rights, equity, and environmental dimensions of AI systems.
- Develop the ability to embed ethics into AI management processes and organizational governance.
Terminology, Concepts, and Lifecycle of Artificial Intelligence
Description: After completing the course, participants will be able to demonstrate the following competences:
- Use standardized AI terminology and definitions correctly.
- Describe and differentiate phases of the AI lifecycle.
- Apply AI lifecycle concepts in risk discussions or system planning.
- Support alignment of AI system activities with ISO/IEC 42001 and ISO/IEC 22989.
- Enable clear internal communication based on shared conceptual understanding.
Previous skills/knowledge: Participants are expected to have the following basic knowledge:
- Basic understanding of digital technologies and computing systems.
- Familiarity with general system lifecycle concepts and terms.
- Introductory exposure to artificial intelligence or automated systems.
Authorized Partners:
Teaching requirements: Trainers should meet the following requirements:
- Subject Matter Expertise: strong foundational knowledge of AI systems, lifecycle models, taxonomy, and ISO/IEC standards including 42001 and 23894.
- Certifications: relevant qualifications in AI governance, system development, or technical standards such as ISO/IEC 42001, ISO/IEC 22989, and ISO/IEC 23894.
- Training & Practical Experience: at least 2 years of experience in explaining AI architecture, terminology, and standards-based AI lifecycle implementation.
Objectives to achieve: The course aims to achieve the following objectives:
- Understand the core terminology and definitions related to AI according to ISO/IEC 22989.
- Gain insight into the structure, stages, and activities of the AI system lifecycle.
- Learn how AI terminology supports communication, risk identification, and compliance efforts.
- Connect concepts of trustworthy AI to lifecycle design and decision-making processes.
- Develop a common conceptual basis for further education or implementation of AI management systems.
Management of Artificial Intelligence Objectives and Risks
Description: After completing the course, participants will be able to demonstrate the following competences:
- Define and document measurable and context-specific AI objectives.
- Identify and assess AI-specific risks based on ISO/IEC 23894 methodology.
- Select and apply appropriate risk treatment measures.
- Align AI objectives and risk decisions with organizational strategy.
- Support continual improvement of the AI management system through risk-based thinking.
Previous skills/knowledge: Participants are expected to have the following basic knowledge:
- Basic understanding of artificial intelligence systems and applications.
- Familiarity with general risk management concepts and terminology.
- Awareness of the role of strategic objectives in governance systems.
Authorized Partners:
Teaching requirements: Trainers should meet the following requirements:
- Subject Matter Expertise: in-depth knowledge of AI governance, ISO/IEC 42001 and ISO/IEC 23894 risk management, and integration with organizational goals.
- Certifications: recommended credentials include ISO/IEC 27005, ISO/IEC 42001, or ISO 31000-related qualifications, particularly in AI or digital technologies.
- Training & Practical Experience: at least 2–3 years of experience in defining risk criteria, conducting AI-specific risk assessments, and aligning objectives with regulatory and business needs.
Objectives to achieve: The course aims to achieve the following objectives:
- Understand how to define objectives for trustworthy and responsible AI in line with ISO/IEC 42001 and ISO/IEC 23894.
- Gain knowledge of AI-specific risk factors and how to assess them using appropriate methodologies.
- Learn how to set, document, and monitor AI objectives in compliance with legal, ethical, and technical expectations.
- Develop skills to perform risk assessments and define risk treatment strategies for AI systems.
- Support the integration of objectives and risks into the AI management system and broader organizational planning.
Reference Objectives and Controls for Artificial Intelligence
Description: After completing the course, participants will be able to demonstrate the following competences:
- Identify and interpret AI reference objectives and control families.
- Select, tailor, and implement appropriate controls for specific AI use cases.
- Document the justification and applicability of AI controls.
- Assess control effectiveness and alignment with AI risk profiles.
- Support organizational compliance, accountability, and AI governance processes.
Previous skills/knowledge: Participants are expected to have the following basic knowledge:
- Basic understanding of artificial intelligence systems and associated risks.
- Familiarity with management system standards and control-based approaches.
- General awareness of ethical, legal, and organizational issues related to AI deployment.
Authorized Partners:
Teaching requirements: Trainers should meet the following requirements:
- Subject Matter Expertise: deep understanding of AI risk management, control frameworks, and alignment with ISO/IEC 42001, ISO/IEC 23894, and ISO/IEC 27002.
- Certifications: recommended certifications include ISO/IEC 27001 Lead Implementer, ISO/IEC 42001 qualifications, or equivalent AI governance and ethics credentials.
- Training & Practical Experience: at least 2–3 years of practical experience in implementing, evaluating, or designing AI-specific controls and mitigation strategies, ideally in regulated or high-impact sectors.
Objectives to achieve: The course aims to achieve the following objectives:
- Understand the role of reference objectives and controls in AI governance.
- Familiarize participants with the structure and use of Annex A in ISO/IEC 42001 and the control families it defines.
- Develop the ability to assess and implement AI-specific controls based on identified risks and system objectives.
- Support the alignment of AI control frameworks with legal, ethical, and performance requirements.
- Enable organizations to select and document applicable controls in support of transparency, robustness, and trustworthiness.
Artificial Intelligence Management
Description: After completing the course, participants will be able to demonstrate the following competences:
- Establish and maintain an AI management system aligned with ISO/IEC 42001,
- Coordinate roles, responsibilities, and communication processes in AI governance,
- Identify, assess, and manage AI-specific risks across system lifecycle,
- Apply and document control measures supporting ethical and compliant AI,
- Contribute to the continuous improvement and monitoring of AI systems in line with stakeholder expectations and regulatory obligations.
Previous skills/knowledge: Participants are expected to have the following basic knowledge:
- Basic understanding of artificial intelligence concepts and terminology,
- Familiarity with information security and risk management frameworks,
- Awareness of legal, organizational, and ethical considerations related to emerging technologies.
Authorized Partners:
Teaching requirements: Trainers should meet the following requirements:
- Subject Matter Expertise: Deep understanding of AI governance, ISO/IEC 42001:2023, ISO/IEC 23894:2023, EU AI Act, and emerging regulatory frameworks.
- Certifications: Recommended credentials include ISO/IEC 27001 Lead Implementer or equivalent in data ethics, AI lifecycle management, or risk management in digital technologies.
- Training & Practical Experience: At least 2–3 years of experience in developing, auditing, or governing AI systems, with familiarity in ethical, legal, and organizational aspects of AI deployment.
Objectives to achieve: The course aims to achieve the following objectives:
- Understand the principles and structure of AI management systems based on ISO/IEC 42001,
- Identify risks, responsibilities, and governance mechanisms required for trustworthy AI,
- Learn how to define AI policies, manage stakeholders, and align AI systems with legal and ethical requirements,
- Gain the ability to apply structured documentation, risk treatment, and continuous improvement processes within AI systems,
- Enable participants to support their organizations in achieving regulatory readiness and accountability in AI deployment.
Artificial Intelligence Risk Management (AIRM) according to ISO/IEC 23894
Description:
- Ability to understand the process approach in Artificial Intelligence Management (ISRM)
- Ability to understand the basic principles and process of the AIRM according to the ISO 23894 guidelines
- Ability to establish and maintain Artificial Intelligence Risk criteria and methods
- Ability to identify the AI requirements of interested parties, threads and vulnerabilities
- Ability to perform the AI Risk Assessment and Treatment according to the ISO 42001 requirements
- Ability to verify the effectiveness of the implemented measures
Previous skills/knowledge:
- Demonstrable knowledge of ISO/IEC 42001 requirements
- Demonstrable knowledge of ISO/IEC 27005 or ISO 31000
- Professional experience, including in information security management systems (recommended).
Authorized Partners:
Teaching requirements:
Trainer to be qualified ISO 42001 – Information technology – Artificial intelligence - Management Systems or qualified ISO/IEC 27005, ISO 31000 Risk Management and ISO/IEC 23894
Objectives to achieve: The participant will identify how to integrate risk management into their activities and functions related to Artificial Intelligence. Additionally, they will describe the processes for the effective implementation and integration of the AI risk management system.
ISO/IEC 42001 Lead Auditor
Description:
- To participate in the selection of the audit team
- To prepare the audit plan
- To represent the audit team before the customer
- To give instructions to the audit team
- To inform any relevant obstacle identified during the audit process
- To be responsible of the all the audits steps
- To present the audit report
- To follow up and close the audit process
Previous skills/knowledge:
- Demonstrable knowledge of ISO/IEC 42001 requirements
- Demonstrable evidence of participation in ISO/IEC 42001 audits (>1 audit recommended)
- Professional experience, including in quality management systems, and information security management systems (recommended).
Authorized Partners:
Teaching requirements:
Trainer to be qualified ISO 42001 – Information technology – Artificial intelligence - Management Systems or to be a qualified ISO/IEC 27001 Lead Auditor and Risk Management.
Objectives to achieve: The participant will learn and apply the main terms, principles, and techniques used during the activities of the audit process, according to the responsibilities assigned to the lead auditor for the review of an Artificial Intelligence Management System (IAMS) ISO/IEC 42001:2023 and based on the guidelines of the ISO 19011:2018 standard for auditing Management Systems.
ISO/IEC 42001 Internal Auditor
Description:
- Follow the instructions of the lead auditor and support them
- Collect and analyse sufficient evidence (for example, through interviews, observation, and documentation sampling) to determine audit findings and define audit conclusions.
- To Document the audit results.
- Collaborate in drafting the audit report.
- Exchange information with other team members and the audited personnel.
Previous skills/knowledge:
- Demonstrable knowledge of ISO/IEC 42001 requirements
- Professional experience in security information, cybersecurity and management systems
Authorized Partners:
Teaching requirements:
Trainer to be qualified ISO 42001 – Information technology – Artificial intelligence - Management Systems or Trainer to be a qualified ISO/IEC 27001 Lead Auditor and Risk Management.
Objectives to achieve: The participant will learn and apply the main terms, principles, and techniques used during the activities of the audit process, according to the responsibilities assigned to the internal auditor for the review of an Artificial Intelligence Management System (IAMS) ISO/IEC 42001:2023 and based on the guidelines of the ISO 19011:2018 standard for auditing Management Systems.
Data Protection Officer
Description: Competences include:
- Perform the work of an authorized Data protection Officer in accordance with the law regulating personal data and the General Data Protection Regulation
- Knew Country and EU regulations on the protection of personal data
- Understood the concept and types of processing of personal data
- Knew the basic principles and basics of inspection law
- Knew the basic principles and basic of administrative law
- Knew the basic principles of compensation law for interference with personal rights and the right to privacy and the protection of personal data
- Able to assess the effects of processing personal data on privacy
- Capable of representing the processor or manager of personal data collections in the inspection procedures of the control body.
Previous skills/knowledge:
- Demonstrable knowledge of the General Data Protection Regulation (GDPR)
- Professional experience from information security
Authorized Partners:
Teaching requirements:
Minimum two teachers; to be qualified for revision of information security (e.g., with auditor/trainer qualification); knowledge of local and EU regulations on the protection of personal data and practice in organizations.
Objectives to achieve: To get the necessary knowledge and skills to:
- Explain the concept of the protection of personal data
- Explain the difference between the protection of personal data and the protection of personal data collections
- Identify the risk in individual types of processing of personal data
- Explain the concept of contractual processing and write a contract of contractual processing with the personal data protection measures included, also knows the rules of the contractual relationship of sub-production
- Explain the concept of cloud services and understand the risk associated with these services
- Explain the concept to build-in privacy and use the concept in various ways of processing personal data
- Take into account the basic principles for handling personal data, transfer them into practice and hand over to internal co-workers
- Keep a record of the processing of personal data processing and identify different risk in the particular types/ processing modes
- Identify processing cases that require an assessment of privacy impacts and create an impact assessment on privacy
- To conduct procedures and to decide on the rights of individuals
- To carry out procedures of internal control of the compliance of the processing of personal data with the law governing the protection of personal data and the General Data Protection Regulation.
Information Security Risks Management (ISRM) according to ISO/IEC 27001:2022 and ISO/IEC 27002:2022
Description:
- Ability to understand the process approach in Information Security Risk Management (ISRM)
- Ability to understand the basic principles and process of the ISRM according to the ISO 27001 requirements, ISO 27002 and ISO 27005 guidelines
- Ability to establish and maintain Information Security Risk criteria and methods
- Ability to identify the IS requirements of interested parties, threads and vulnerabilities
- Ability to perform the IS Risk Assessment and Treatment according to the ISO 27001 requirements
- Ability to verify the effectiveness of the implemented measures
Previous skills/knowledge: Basic knowledge of management systems.
Authorized Partners:
Teaching requirements:
Trainer to be a qualified ISO9001/QMS and/or other MS with knowledge in ISMS, Risk Management and/or ISO 27001 Lead Auditor (Recommended)
Objectives to achieve: To get the necessary knowledge and skills to:
- Be able to understand the process approach in Information Security Risk Management
- Be able to understand the ISO 27001 standard requirements regarding ISRM
- Be able to perform the IS Risk Assessment and Treatment
- Be able to formulate Risk Treatment Plan and effectively manage Information Security Risks