Sovereign AI for Regulated Organizations: Controlling Data, Models and Inference Environments Training Course

Foundations of Sovereign AI

• Understanding the meaning of sovereign AI in regulated organizations.
• Business, legal, and operational drivers.
• Key control areas: data, models, infrastructure, and operations.

Regulatory Requirements and Risk Mapping

• Data residency, privacy, and sector-specific obligations.
• Mapping sensitive data to specific AI use cases.
• Identifying risks related to cross-border data transfer, logging, and third-party exposure.

Governing Data, Prompts, and Logs

• Prompt governance and defining acceptable use boundaries.
• Logging policies for prompts, responses, and metadata.
• Practices for retention, redaction, masking, and access control.
• Exercise: reviewing an AI data flow to identify governance gaps.

Model Hosting and Inference Environment Options

• Deployment choices: public API, private cloud, on-premise, and hybrid environments.
• Key factors influencing where models should run.
• Balancing trade-offs among control, security, cost, and operational ownership.

Vendor Dependence and Portability

• Common patterns of vendor lock-in in models, tools, and platforms.
• Achieving portability through modular architecture, open interfaces, and clear contractual terms.
• Exercise: evaluating a vendor against sovereignty criteria.

Governance Model and Action Planning

• Defining roles and responsibilities across IT, security, legal, and compliance teams.
• Approval workflows for use cases, models, and operational changes.
• Expectations for auditability, monitoring, and incident response.
• Creating a practical sovereign AI roadmap and identifying next steps.