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

DataClouddatabasespipelinesAPIsdata streamsaccess rightstraceability

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

trainingvalidationmetricsbiasexplainabilitydriftmonitoring

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

Foundation ModelsembeddingsRAGvector basesmultimodalityhallucinationsevaluation

Objective: To understand what changes when AI goes beyond simply responding and begins to take action.

agentstoolsAPIsmemorypermissionsactionsmulti-agentHuman-in-the-looptraces

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

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

Personal InformationLegal BasesPurposesProfilingAutomated DecisionsRightsAccountability

Goal: To make data governance the first step toward trustworthy AI.

Data GovernanceQualitylineagedocumentationretentionauthorizationsPrivacy by Design

Objective: To incorporate issues of sovereignty and supplier dependence into governance decisions.

international transfersextraterritorialitylocationCloudtechnological dependenciesreversibility

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

DPIAsecurityData ProtectionData QualityDocumentationAuditsevidenceRemediation

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 the BC01 qualification under the RNCP Level 7 "Project Manager in Artificial Intelligence Solutions Development" certification.

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

scopeStakeholdersRisk LevelsProhibited PracticesHigh-Risk SystemsTransparency

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

ProvidersDeployersGPAIDocumentationHuman Oversightmonitoringincidentsevidence

Objective: To establish a legal framework for new AI value chains.

training datacopyrightuser-generated contenttrade secretsliabilityAI clausessuppliers

Objective: To compare the major regulatory models for managing technologies, data, and uses deployed across multiple jurisdictions.

European UnionUnited StatesUnited KingdomChinaIndiaUnited Arab Emirates

Objective: To develop a comprehensive AI governance framework.

AI InventoryrolesResponsibilitiesCommitteesPoliciesLife CycleHuman Oversightdocumentation

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 RiskModel RiskLLM RiskAgent RiskcyberPrivacybiasreputationsuppliers

Objective: To incorporate the ethical limits of a system beyond mere compliance.

fairnesstransparencyexplainabilityfundamental rightshuman autonomydiscriminationproportionality

One question underlies 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: Evaluate a system based on five dimensions: Legal, Risk, Ethics, Trust, and Human Oversight.

Objective: To move from declared compliance to demonstrated compliance.

architecturedatamodelsRAGagentspermissionslogstracesmonitoringauditability

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

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

searchmonitoringwritingcontractsdue diligenceliterature reviewLegal Designknowledge management

Objective: To build a specialized assistant capable of applying in-depth legal knowledge.

RAGknowledge basessourcesPermissionsPrivacyEvaluationguardrails

Objective: To design agents capable of operating within a controlled legal framework.

monitoringcontractsqualificationcollectionAlertsactionshuman validationmulti-agentaudit trail

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

LegalTechRegTechRegulatory IntelligenceCompliance MonitoringObligation Mappingalerting

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

Cloudproprietary / open-source modelsAPIssupply chain AIlocalizationreversibilitythird-party risk

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

data leakcritical hallucinationdiscriminationagent out of controlsupplier incidentreputation

Scenario: AI Crisis Room.

Goal: To move from one-off projects to a true AI governance function.

Operating ModelMaturityroadmapKPI/KRIcontrolsbudgetcommitteesreportingchange management

Objective: As AI automates research, analysis, and writing, create greater value through judgment, interpretation, decision-making, and accountability.

Legal OperationsAutomationHuman/AI AllocationNew WorkflowsNew RolesskillsTarget Organization

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