Objective: To understand how data flows and how the architecture of an AI system is organized.

Data Cloud Databases Pipelines APIs Data Flows Access Rights Traceability

Objective: to gain sufficient understanding of how a model works to be able to evaluate its design, performance, and risks.

Training Validation Metrics Bias Explainability Drift Monitoring

Objective: To understand new generative architectures and the challenges they pose in terms of privacy, reliability, intellectual property, and accountability.

Foundation Models Embeddings RAG Vector Databases Multimodality Hallucinations Evaluation

Objective: To understand what changes when AI no longer merely responds but begins to act.

Agents Tools APIs Memory Permissions Actions Multi-Agents Human-in-the-Loop Traces

AI GRC Professional Project #1 — AI Technology & Impact Assessment: Analyze an AI system in terms of its architecture, data, uses, stakeholders, impacts, risks, and liability.

Objective: Apply the data protection framework to the specific characteristics of AI systems.

Personal Data Legal Bases Purposes Profiling Automated Decisions Rights Accountability

Objective: Make data governance the first step toward trustworthy AI.

Data Governance Quality Lineage Documentation Retention Access Controls Privacy by Design

Objective: to integrate issues of sovereignty and vendor dependency into governance decisions.

International Data Transfers Extraterritoriality Localization Cloud Technological Dependencies Reversibility

Objective: To establish data compliance for an AI project, from mapping to remediation.

DPIA Security Data Protection Data Quality Documentation Controls Evidence Remediation

AI GRC Professional Project #2 — Data Protection & Quality Governance Plan: data mapping, risks, protection, quality, governance, and action plan.

This project contributes to the validation of BC01 for the RNCP Level 7 certification for Project Manager in Artificial Intelligence Solution Development.

Objective: To learn how to determine what constitutes an AI system from a legal perspective and what obligations apply to it.

Scope of Application Stakeholders Risk Levels Prohibited Practices High-Risk Systems Transparency

Objective: To translate regulatory requirements into concrete actions for technical and business teams.

Providers Deployers GPAI Documentation Human Oversight Monitoring Incidents Evidence

Objective: To legally secure new AI value chains.

Training Data Copyright Generated Content Trade Secrets Liability AI Clauses Vendors

Objective: To compare major regulatory models in order to manage technologies, data, and uses deployed across multiple jurisdictions.

European Union United States United Kingdom China India United Arab Emirates

Objective: to build a comprehensive AI governance framework.

AI Inventory Roles Responsibilities Committees Policies Lifecycle Human Oversight Documentation

AI GRC Professional Project #3 — AI Governance Framework: registry, responsibilities, policies, controls, oversight, and documentation.

Objective: To identify, assess, prioritize, and address risks associated with AI systems.

Data Risk Model Risk LLM Risk Agent Risk Cybersecurity Privacy Bias Reputation Vendors

Objective: to integrate the ethical boundaries of a system beyond mere compliance.

Fairness Transparency Explainability Fundamental Rights Human Autonomy Discrimination Proportionality

One question underpins the entire seminar: "Can we do it?" is not the same question as "Should we do it?"

AI GRC Professional Project #4 — AI Risk, Ethics & Trust Assessment: evaluating a system across five dimensions: Legal, Risk, Ethics, Trust, and Human Oversight.

Objective: to move from declared compliance to demonstrated compliance.

Architecture Data Models RAG Agents Permissions Logs Traces Monitoring Auditability

AI GRC Professional Project #5 — AI System Audit & Control Report: Conduct a technology-informed GRC audit and develop a remediation plan.

Objective: Learn how to use AI without delegating legal judgment to it.

Research Monitoring Drafting Contracts Due Diligence Document Analysis Legal Design Knowledge Management

Objective: Build a specialized assistant capable of leveraging expert legal knowledge.

RAG Knowledge Bases Sources Permissions Confidentiality Evaluation Guardrails

Objective: to design agents capable of acting within a controlled legal framework.

Monitoring Contracts Qualification Data Collection Alerts Actions Human Validation Multi-Agent Systems Audit Trail

Objective: to build systems capable of detecting regulatory changes, identifying their impacts, and triggering the appropriate actions.

LegalTech RegTech Regulatory Intelligence Compliance Monitoring Obligations Mapping Alerting

Objective: To assess not only a supplier's compliance but also the strategic dependence it creates.

Cloud Proprietary/Open-Source Models APIs AI Supply Chain Localization Reversibility Third-Party Risk

Objective: to detect, classify, document, make decisions, remediate, and communicate in response to an AI incident.

Data Breach Critical Hallucination Discrimination Out-of-Control Agent Supplier Incident Reputation

Scenario: AI Crisis Room.

Objective: To transition from ad-hoc projects to a fully-fledged AI governance function.

Operating Model Maturity Roadmap KPIs/KRIs Controls Budget Committees Reporting Change Management

Objective: As AI automates research, analysis, and drafting, create greater value through judgment, interpretation, arbitration, and accountability.

Legal Operations Automation Human/AI Allocation New Workflows New Roles Skills Target Organization

AI GRC Professional Project #6 — Legal AI & GRC Transformation Blueprint: assessment → technology → law → risks → ethics → governance → stakeholders → target organization → roadmap.