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

Foundations of Sovereign AI

• Understanding what sovereign AI entails for regulated organisations
• Business, legal, and operational drivers
• Key control areas: data, models, infrastructure, and operations

Regulatory Requirements and Risk Mapping

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

Governing Data, Prompts, and Logs

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

Model Hosting and Inference Environment Options

• Evaluating deployment choices: public API, private cloud, on-premise, and hybrid environments
• Key factors for determining where models should operate
• Balancing trade-offs among control, security, cost, and operational ownership

Vendor Dependence and Portability

• Recognising common lock-in patterns 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
• Establishing approval workflows for use cases, models, and operational changes
• Meeting expectations for auditability, monitoring, and incident response
• Developing a practical sovereign AI roadmap and outlining next steps