EU AI Act Compliance Training

Lesson 1 • Timeline and Entry into Force

Maps the phased implementation schedule and key compliance deadlines. Employees understand urgency and can prioritize preparation activities.

Lesson 2 • Territorial and Material Scope

Defines which organizations, systems, and geographies fall under the regulation. Employees learn whether their employer and tools are covered.

Lesson 3 • Origins and Policy Context

Traces the regulatory gap that prompted the law and the policy goals it addresses. Grounds subsequent compliance topics in real legislative intent.

Lesson 4 • Core Objectives and Principles

Explains the fundamental values—safety, transparency, accountability—embedded in the regulation. Provides the ethical lens applied throughout the course.

Lesson 1 • Prohibited AI Practices

Details AI applications banned outright due to unacceptable societal harm. Employees recognize and refuse to deploy or support prohibited systems.

Lesson 2 • Limited and Minimal Risk Systems

Distinguishes systems with transparency obligations from those with no mandatory requirements. Employees avoid over-compliance costs and under-compliance gaps.

Lesson 3 • Classifying Your Organization's AI

Applies the classification framework to real workplace scenarios through structured exercises. Employees build confidence in making accurate, defensible risk determinations.

Lesson 4 • High-Risk AI Systems

Covers the two categories of high-risk systems and their listed use cases. Employees identify high-risk deployments requiring full compliance obligations.

Lesson 1 • Shared Responsibility Scenarios

Analyzes cases where provider and deployer obligations overlap or conflict. Employees negotiate and document responsibility splits with vendors and partners.

Lesson 2 • Authorized Representatives

Explains the role of EU-based representatives for non-EU providers. Employees in multinational firms understand how cross-border accountability is structured.

Lesson 3 • Deployer Duties and Limits

Covers what organizations using third-party AI systems must do independently of the provider. Employees understand they cannot delegate all responsibility to vendors.

Lesson 4 • Importer and Distributor Roles

Defines the narrower but real obligations of supply-chain actors who do not develop AI. Prevents compliance gaps when sourcing AI from non-EU vendors.

Lesson 1 • Conformity Assessment Pathways

Explains self-assessment versus third-party audit routes for demonstrating compliance. Employees select the correct pathway and prepare supporting evidence.

Lesson 2 • Transparency and User Information

Specifies what information must be disclosed to deployers and end users of high-risk systems. Employees draft compliant instructions for use and interface disclosures.

Lesson 3 • Human Oversight Mechanisms

Defines the technical and procedural controls enabling humans to monitor, intervene, and override AI decisions. Employees design oversight workflows for their specific context.

Lesson 4 • Technical Documentation Standards

Details the mandatory documentation package that must accompany every high-risk system. Employees produce and maintain records that satisfy regulatory audits.

Lesson 5 • Data Governance and Quality

Covers requirements for training, validation, and testing datasets used in high-risk systems. Links data quality directly to system safety and legal compliance.

Lesson 1 • Codes of Practice and Governance

Describes the industry-led codes of practice as a compliance pathway for general-purpose AI. Employees engage constructively with evolving standards and governance bodies.

Lesson 2 • Systemic Risk Classification

Identifies when a general-purpose model crosses the systemic risk threshold and what additional duties apply. Employees at large AI providers understand heightened obligations.

Lesson 3 • Provider Transparency and Documentation

Covers the technical documentation and model card requirements for general-purpose AI providers. Employees producing or procuring such models know what records to demand.

Lesson 4 • Defining General-Purpose AI Models

Distinguishes general-purpose AI from task-specific systems and explains why separate rules apply. Provides the conceptual foundation for all obligations in this chapter.

Lesson 1 • Transparency Obligations by Risk Tier

Maps specific disclosure duties to each risk classification established earlier. Employees apply the right transparency standard without over- or under-disclosing.

Lesson 2 • Maintaining Transparency Over Time

Addresses how transparency obligations persist through system updates and changing use cases. Employees build processes to keep disclosures accurate and current.

Lesson 3 • Explainability Techniques for Non-Experts

Introduces practical methods for communicating how AI systems reach outputs to non-technical audiences. Employees translate model behavior into plain-language explanations.

Lesson 4 • Explainability in High-Stakes Decisions

Focuses on contexts where AI outputs affect employment, credit, or access to services. Employees provide meaningful explanations that support individuals' rights to contest decisions.

Lesson 1 • Building an AI Governance Framework

Outlines the structural components of an effective internal AI governance program. Employees connect regulatory requirements to existing corporate governance mechanisms.

Lesson 2 • Regulatory Engagement and Market Surveillance

Prepares employees to interact with national competent authorities and market surveillance bodies. Proactive engagement reduces enforcement risk and builds regulatory trust.

Lesson 3 • Incident Reporting and Serious Incident Handling

Defines what constitutes a serious incident and the mandatory reporting timeline to authorities. Employees execute rapid, accurate incident responses that meet legal deadlines.

Lesson 4 • Post-Market Monitoring and Logging

Details the ongoing surveillance obligations after an AI system is deployed. Employees design monitoring plans that detect performance drift and emerging risks.

Lesson 5 • Quality Management Systems for AI

Covers the quality management system requirements mandated for high-risk AI providers. Employees adapt existing ISO-aligned QMS processes to meet AI-specific obligations.

Lesson 1 • Compliance Roadmap and Resource Planning

Guides employees in translating gap analysis results into a funded, time-bound action plan. Connects compliance investment to business risk reduction and competitive positioning.

Lesson 2 • Enforcement Architecture

Maps the multi-level enforcement system involving national authorities, the AI Office, and the AI Board. Employees understand who investigates, who decides, and who appeals.

Lesson 3 • Penalty Tiers and Calculation Factors

Details the three-tier fine structure and the factors authorities weigh when setting penalties. Employees quantify financial exposure and justify investment in compliance programs.

Lesson 4 • Conducting a Compliance Gap Analysis

Provides a structured method for assessing current state against all regulatory requirements. Employees produce a prioritized remediation roadmap from gap analysis outputs.