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

Foundations of Sovereign AI

• Understanding sovereign AI within the context of regulated organizations.
• Business, legal, and operational drivers behind adoption.
• Key control domains: data, models, infrastructure, and operations.

Regulatory Requirements and Risk Mapping

• Data residency mandates, privacy regulations, and industry-specific obligations.
• Linking sensitive data types to specific AI use cases.
• Identifying risks related to cross-border data flows, logging practices, and third-party exposure.

Governing Data, Prompts, and Logs

• Establishing prompt governance and acceptable usage boundaries.
• Implementing logging policies for prompts, responses, and metadata.
• Best practices for retention, redaction, masking, and access control.
• Exercise: Analyzing 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 models.
• Key factors influencing where models should operate.
• 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: Assessing a vendor against sovereignty criteria.

Governance Model and Action Planning

• Defining roles and responsibilities across IT, security, legal, and compliance teams.
• Structuring approval workflows for use cases, models, and operational changes.
• Expectations for auditability, monitoring, and incident response.
• Constructing a practical sovereign AI roadmap and defining next steps.