Why take this course
Would you like to prepare your employees for the upcoming transformation in AI regulations?
This training provides comprehensive knowledge of the European Union’s AI Act while demonstrating how to use AI tools in a practical, ethical, and secure manner in everyday work. The course was specifically designed for employers seeking to meet the requirements of Chapter IV of the EU Artificial Intelligence Act.
What you'll learn
After completing this course, participants will be able to:
- Understand the fundamental definitions and operating principles of AI systems.
- Learn the requirements of the EU AI Act, including key implementation timelines and risk categories.
- Understand human oversight requirements (HITL, HOTL, and HOOTL).
- Identify the obligations of organizations and AI system users (deployers).
- Apply GDPR-compliant data processing principles in AI-related activities.
- Master the fundamentals of prompt engineering.
- Evaluate AI model outputs and identify potential bias and fairness issues.
Certification & Exam
Upon completion of the training, participants receive a Certificate of Attendance confirming their knowledge of the legal and technical aspects of AI required by the EU AI Act.
The course does not include a final examination.
Who is this course for
This training is intended for:
- Managers and team leaders who want to prepare their organizations for AI Act compliance.
- IT professionals and software developers working with AI models and AI-powered solutions.
- Legal, compliance, and risk management professionals.
- Employees of organizations seeking to meet the requirements of Chapter IV of the AI Act.
Topics covered
- “AI Foundations Quick Start” – Building a Common Foundation of AI Knowledge
- Definition of AI and the difference between rule-based systems and machine learning (comparison table).
- Six everyday examples of AI (voice assistants, spam filters, video streaming recommendations, etc.).
- Key acronyms and terminology (ML, NLP, GPAI, HITL, etc.).
- Mini knowledge quiz (K1/K2).
- AI Fundamentals and the Scope of the AI Act
- Definition of an AI system according to the AI Act; categories and practical use cases.
- Risk categories: prohibited, high-risk, limited-risk, and minimal-risk systems.
- Roles and responsibilities (provider, deployer, importer, distributor).
- AI literacy obligations and the principle of proportionality.
- EU AI Act timeline: 2024 → 2027.
- Extraterritorial scope of the regulation (providers outside the EU).
- Opportunities and criticism of the regulation (innovation, SME compliance costs).
- Risk Identification and Human Oversight
- How to identify a high-risk AI system.
- Human oversight requirements and decision documentation.
- Cheat sheet: HITL / HOTL / HOOTL – definitions, examples, and intervention procedures.
- Impact assessments (DPIA/FRIA) – when and how to conduct them.
- Error and “hallucination” scenarios – escalation procedures.
- Responsibilities of Organizations and Users (Deployers)
- Minimum organizational requirements: policies, registers, and procedures.
- Documentation of training, authorizations, and access rights.
- Use case inventory register – sample template.
- Cooperation with AI vendors (SaaS, foundation models, APIs) – contracts and compliance considerations.
- Interaction with sector-specific regulations (MDR, PSD2, transportation, cybersecurity).
- Law and Ethics: GDPR + Transparency
- Data processing principles (lawful basis, data minimization).
- Privacy by Design / Privacy by Default and security requirements (Articles 25 and 32 GDPR).
- Non-discrimination, bias mitigation, and training data governance.
- “AI Transparency Playbook” (Article 52):
- Obligation to inform users when interacting with AI.
- Labeling AI-generated content.
- Disclosure of biometric data usage.
- Templates: chatbot notification banners and image watermarking.
- Relationship between the AI Act and GDPR – overlapping and distinct requirements.
- Prompt Engineering Fundamentals
- The LLM mental model: role, context, task, format, and evaluation criteria.
- Structure of a “good prompt” (RKTFF framework).
- Limitations, quality metrics, and token hygiene.
- Advanced Prompt Engineering Techniques and Business Scenarios
- Example-based learning, chain-of-thought prompting, and self-reflection techniques.
- Prompt chaining and orchestration (agent-based workflows).
- Prompt templates – versioning and corporate repositories.
- Best and worst practices: disclosure versus anonymization of business secrets and sensitive information.
- Validation, Quality Assessment, and Auditability
- Checklists and evaluation rubrics for AI model outputs.
- Traceability and prompt version control.
- Bias & Fairness Casebook – three scenarios (HR, lending, healthcare) with bias mitigation exercises.
- Compliance checklist (business, legal, IT, and security perspectives).
- Maintaining Compliance and Developing Competencies
- Mechanisms for continuous upskilling (regulatory, policy, and model updates).
- Training update plans – monitoring regulatory guidance and AI tool developments.
- Support channels: internal AI Office, knowledge base, and Q&A resources.
This training syllabus is designed to address the requirements outlined in the European Union Artificial Intelligence Act (AI Act).
