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.
• Key business, legal, and operational drivers.
• Core areas of control: data, models, infrastructure, and operations.

Regulatory Requirements and Risk Mapping

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

Governing Data, Prompts, and Logs

• Prompt governance and establishing acceptable use boundaries.
• Logging policies for prompts, responses, and metadata.
• Best practices for retention, redaction, masking, and access control.
• 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 solutions.
• Key factors in determining where models should run.
• Balancing trade-offs among control, security, cost, and operational ownership.

Vendor Dependence and Portability

• Recognizing 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.
• Maintaining auditability, monitoring capabilities, and incident response protocols.
• Constructing a practical sovereign AI roadmap and defining next steps.