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FORMATION IA

Conformité SOC 2 pour les produits IA et Machine Learning

Concevez, documentez et défendez les contrôles SOC 2 appliqués à vos pipelines IA et ML.

Format
programme
Durée
16–24h
Niveau
practitioner
Taille de groupe
4–16
Prix / participant
€2K–€4K
Prix groupe
€12K–€28K
Public
Compliance officers, CTOs, GRC teams, and security engineers at companies building or integrating AI products
Prérequis
Working knowledge of SOC 2 fundamentals and familiarity with at least one AI or ML product in production or near-production

Ce qu'elle couvre

Ce programme de niveau praticien couvre l'application des critères de confiance SOC 2 aux systèmes d'IA et de machine learning, notamment les pipelines d'entraînement, l'infrastructure d'inférence et les intégrations de fournisseurs IA tiers. Les participants apprennent à cartographier les risques spécifiques à l'IA sur les contrôles SOC 2, à constituer des dossiers de preuves prêts pour l'audit, et à dialoguer efficacement avec les auditeurs sur la provenance des données, la dérive des modèles et les décisions automatisées. Les sessions combinent des ateliers de conception de contrôles et des scénarios d'audit réels issus d'environnements de produits IA.

À l'issue, vous saurez

  • Map each SOC 2 Trust Services Criterion to concrete controls within your organisation's AI and ML pipelines
  • Produce an audit-ready evidence package covering model lifecycle, data handling, and access controls
  • Conduct a structured third-party AI vendor risk assessment aligned to SOC 2 vendor management requirements
  • Design monitoring and alerting procedures for model drift and inference anomalies that satisfy auditor expectations
  • Identify and remediate the top five control gaps that cause AI companies to fail or receive qualified SOC 2 Type II opinions

Sujets abordés

  • Mapping SOC 2 Trust Services Criteria (Security, Availability, Confidentiality, Processing Integrity, Privacy) to AI/ML contexts
  • Control design for model training pipelines and feature stores
  • Third-party AI vendor risk assessment and due diligence
  • Data provenance, lineage, and retention controls for training data
  • Model change management, version control, and rollback evidence
  • Monitoring and alerting for model drift and anomalous inference behaviour
  • Audit evidence collection: logs, dashboards, and approval records for ML workflows
  • Incident response and breach notification obligations in AI-enabled products

Modalité

Delivered as a 2–3 day instructor-led programme, available in-person or live virtual. Each session is roughly 60% hands-on: participants work on their own control matrices and evidence templates using provided AI-specific SOC 2 workbooks. A pre-work questionnaire captures participants' current tech stack and auditor relationship status so examples are tailored. Remote delivery uses Miro for collaborative control mapping and a shared document workspace for evidence artefact drafting. Physical delivery includes printed workbooks and a half-day tabletop audit simulation on day two.

Ce qui fait que ça marche

  • Assign a named control owner for each AI pipeline stage before the audit window opens
  • Automate evidence collection from CI/CD and model registry tools so logs are audit-ready without manual effort
  • Conduct an internal readiness review at the 60-day mark of the observation period using the same criteria an auditor would apply
  • Maintain a living third-party AI vendor inventory updated at every contract renewal or model version change

Erreurs fréquentes

  • Treating AI vendors as generic SaaS vendors and failing to assess model training data access and output data retention separately
  • Using generic SOC 2 control templates that never mention ML pipelines, leaving auditors unable to test against actual system behaviour
  • Assigning all AI-related controls to engineering without involving compliance or legal, creating ownership gaps that surface during fieldwork
  • Collecting point-in-time screenshots as evidence rather than continuous log exports, which fails Type II coverage requirements

Quand NE PAS suivre cette formation

This programme is not the right fit for teams that have not yet chosen a cloud infrastructure or AI stack — the control design exercises require a concrete system to map against, and teams still in ideation will derive little actionable value.

Fournisseurs à considérer

Sources

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