EU AI Act Compliance Training

Lesson 1 • Structure and Scope of the Act

Maps the Act's territorial reach, subject-matter scope, and key exclusions. Learners distinguish what systems and actors fall inside or outside coverage.

Lesson 2 • Core Definitions and Key Concepts

Introduces statutory definitions essential for applying the Act correctly. Precise terminology prevents misclassification of systems and roles.

Lesson 3 • The Act Within Global AI Governance

Positions the Act relative to international standards and comparable frameworks. Learners recognise convergence points and unique EU-specific requirements.

Lesson 1 • General-Purpose AI Model Tiers

Addresses the distinct classification track for general-purpose AI models, including systemic-risk thresholds. Learners differentiate model-level from system-level obligations.

Lesson 2 • High-Risk AI Systems Defined

Explains criteria and sector-specific annexes that designate high-risk status. Learners map their own systems against each criterion systematically.

Lesson 3 • Limited and Minimal Risk Categories

Covers transparency obligations for limited-risk systems and the voluntary path for minimal-risk systems. Learners avoid over-compliance and under-compliance errors.

Lesson 4 • Prohibited AI Practices

Details the absolute prohibitions and the harms they prevent. Learners apply criteria to identify banned systems before any further classification.

Lesson 1 • Importer and Distributor Roles

Explains the verification and pass-through obligations for supply chain actors. Learners avoid assuming provider status unintentionally.

Lesson 2 • Deployer Duties and Accountability

Covers obligations for organisations that use high-risk AI in their operations. Learners identify where deployer duties begin when provider documentation is absent.

Lesson 3 • Shared and Overlapping Responsibilities

Addresses scenarios where multiple parties share compliance duties for a single system. Learners draft contractual arrangements that allocate responsibilities clearly.

Lesson 1 • Data and Data Governance Standards

Specifies training, validation, and testing data requirements under the Act. Learners apply data governance practices that satisfy regulatory and quality standards simultaneously.

Lesson 2 • Human Oversight Mechanisms

Defines technical and organisational measures that enable effective human control. Learners design oversight workflows that prevent automation bias.

Lesson 3 • Accuracy, Robustness, and Cybersecurity

Sets performance and security standards that high-risk systems must meet throughout their lifecycle. Learners apply testing regimes that validate ongoing compliance.

Lesson 4 • Transparency and User Information

Covers instructions for use, capability disclosures, and AI-generated content labelling. Learners draft user-facing materials that meet transparency obligations.

Lesson 1 • Conformity Assessment Pathways

Distinguishes self-assessment from third-party notified body assessment and the criteria for each. Learners select the correct pathway for their system category.

Lesson 2 • EU Declaration of Conformity

Covers the mandatory content, signatory requirements, and retention rules for the declaration. Learners draft declarations that satisfy all formal requirements.

Lesson 3 • Notified Bodies: Selection and Process

Explains notified body accreditation, scope, and the audit process they conduct. Learners prepare submissions that minimise assessment delays.

Lesson 1 • Systemic Risk Identification and Assessment

Details how to determine whether a model crosses the systemic-risk threshold and what that triggers. Learners conduct capability evaluations and document findings.

Lesson 2 • Downstream Provider Obligations

Explains how general-purpose AI model providers must support downstream system providers. Learners design information-sharing and contractual arrangements accordingly.

Lesson 3 • Systemic Risk Mitigation Measures

Covers the additional safety, incident reporting, and cybersecurity obligations for systemic-risk models. Learners build mitigation frameworks proportionate to identified risks.

Lesson 4 • Copyright and Training Data Policy

Covers obligations to respect copyright law and publish training data policies. Learners implement data sourcing practices that reduce legal exposure.

Lesson 1 • National Competent Authorities

Maps the roles of market surveillance authorities and notifying authorities at the national level. Learners identify which authority oversees their sector and system type.

Lesson 2 • Penalty Structure and Liability

Details the tiered fine structure, aggravating and mitigating factors, and SME provisions. Learners calculate potential exposure and prioritise compliance investments accordingly.

Lesson 3 • EU-Level Supervisory Bodies

Covers the AI Office, the AI Board, and the scientific panel and their respective mandates. Learners distinguish EU-level from national-level enforcement actions.

Lesson 4 • Regulatory Sandboxes and Innovation Support

Explains the sandbox mechanism that allows controlled testing under regulatory supervision. Learners assess eligibility and prepare sandbox applications.

Lesson 1 • Compliance Programme Maturity Model

Introduces a maturity framework for benchmarking and advancing the compliance programme over time. Learners assess current maturity and set measurable improvement targets.

Lesson 2 • Gap Analysis and Remediation Planning

Provides a structured approach to identifying compliance gaps and prioritising remediation. Learners produce actionable roadmaps with resource and timeline estimates.

Lesson 3 • AI Inventory and Risk Mapping

Establishes methods for cataloguing all AI systems in use and assigning risk tiers. A complete inventory is the foundation of every subsequent compliance activity.

Lesson 4 • Policies, Procedures, and Training

Covers the internal policy suite and staff training programmes required to operationalise compliance. Learners design role-specific training curricula and policy review cycles.