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

Foundations of Sovereign AI

• Understanding what sovereign AI means for regulated organizations.
• Business, legal, and operational drivers behind sovereignty.
• Core control areas: data, models, infrastructure, and operations.

Regulatory Requirements and Risk Mapping

• Data residency, privacy laws, 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.
• Establishing 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

• Evaluating deployment choices: public APIs, private cloud, on-premise, and hybrid models.
• Key factors for deciding where models should run.
• Weighing 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 contracts.
• 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.
• Building a practical sovereign AI roadmap and outlining next steps.