This Deployer due diligence of Provider GPAI course addresses the essential responsibilities and due diligence processes for deployers integrating General Purpose AI (GPAI) models, particularly those posing systemic risks, into their operations.

Course Overview

This Deployer due diligence of Provider GPAI course draws from the evolving AI regulatory landscape, including the EU AI Act and related codes of practice, to equip participants with a clear understanding of indirect obligations, risk management strategies, and best practices for compliant and responsible GPAI deployment. Through a structured exploration of provider verification, transparency, intellectual property, safety measures, and operational readiness, learners will gain actionable insights to navigate complex compliance challenges while minimizing legal, reputational, and operational risks.

This course will equip participants with a sharper perspective on organizational pitfalls, emphasizing proactive strategies to ensure integrity, compliance, and reliability in AI initiatives.

Learning Objectives

By the end of this course, participants will be able to:

• Identify and verify GPAI providers' compliance, including legal entity details, codes of practice adherence, and systemic risk classifications.
• Assess exemptions for open-source models and evaluate internal provider governance structures for accountability.
• Evaluate transparency and documentation requirements, ensuring access to up-to-date model information and effective information-sharing mechanisms.
• Navigate copyright compliance, including text and data mining exceptions, opt-out mechanisms, and output safeguards to mitigate infringement risks.
• Analyze safety and security frameworks, risk identification, mitigations, and collaborative efforts to manage systemic harms.
• Conduct context-specific risk assessments for GPAI integration, avoiding unintended role shifts from deployer to provider.
• Implement contractual clauses, usage restrictions, and operational practices for high-risk systems, including content labeling and regulatory sandboxes.

Key Topics Covered

• Provider Identification and Compliance: Fundamentals of verifying provider details, traceability, and adherence to codes of practice.
• Systemic Risk Classification: Evidence for risk thresholds, notifications, and implications for deployers.
• Exemptions and Open-Source Models: License audits, role shifts, and avoiding hidden risks.
• Internal Governance and Accountability: Roles, contacts, and structures for effective risk management.
• Transparency and Documentation: Model forms, updates, retention, and information requests.
• Copyright and Intellectual Property Compliance: TDM exceptions, opt-outs, output safeguards, and redress mechanisms.
• Safety and Security Measures: Frameworks, mitigations, cybersecurity, and continuous assessments.
• Integration Risk Assessment: Context-specific scenarios, evaluations, and high-risk obligations.
• Contractual and Operational Readiness: Usage restrictions, instructions, confidentiality, and SME proportionality.
• Transparency for Users and Testing: Content labeling, sandboxes, and building trust in AI ecosystems.

This course empowers deployers to foster safer AI integrations, ensuring alignment with ethical standards and regulatory demands while promoting innovation.

Target Audience: This course is designed for AI deployers, compliance officers, legal professionals, IT managers, and executives in organizations integrating GPAI models. It is particularly relevant for SMEs, enterprises in high-stakes sectors (e.g., finance, healthcare, critical infrastructure), and those operating within the EU regulatory framework. No prior AI expertise is required, but familiarity with basic regulatory concepts is beneficial.