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

Foundations of Sovereign AI

• Interpreting sovereign AI in the context of regulated organizations
• Key business, legal, and operational motivators
• Primary control domains: data, models, infrastructure, and operations

Regulatory Mandates and Risk Assessment

• Data residency, privacy laws, and industry-specific regulatory obligations
• Mapping sensitive data to specific AI use cases
• Identifying risks related to cross-border data flows, logging, and third-party exposure

Governing Data, Prompts, and Logs

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

Model Hosting and Inference Environment Strategies

• Deployment options: public APIs, private clouds, on-premise, and hybrid models
• Key factors for determining optimal model execution environments
• Balancing trade-offs among control, security, cost, and operational ownership

Mitigating Vendor Dependence and Ensuring Portability

• Recognizing common lock-in patterns in models, tools, and platforms
• Achieving portability via modular architecture, open interfaces, and explicit contractual terms
• Practical exercise: assessing a vendor against specific sovereignty criteria

Governance Frameworks and Strategic Planning

• Defining roles and responsibilities across IT, security, legal, and compliance teams
• Designing approval workflows for use cases, models, and operational modifications
• Setting expectations for auditability, continuous monitoring, and incident response
• Formulating a practical sovereign AI roadmap and identifying immediate next steps