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.
• Identifying business, legal, and operational drivers.
• Focusing on core control areas: data, models, infrastructure, and operations.

Regulatory Requirements and Risk Mapping

• Addressing data residency, privacy concerns, 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

• Establishing prompt governance and acceptable use boundaries.
• Defining logging policies for prompts, responses, and metadata.
• Implementing 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 such as public APIs, private clouds, on-premise solutions, and hybrid models.
• Determining key factors for deciding where models should operate.
• Analyzing trade-offs regarding 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 departments.
• Establishing approval workflows for use cases, models, and operational changes.
• Setting expectations for auditability, monitoring, and incident response.
• Creating a practical sovereign AI roadmap and determining next steps.