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

Sovereign AI Fundamentals

• Understanding the significance of sovereign AI in regulated organizations.
• Business, legal, and operational motivations.
• Key areas of control: data, models, infrastructure, and operations.

Regulatory Obligations and Risk Assessment

• Data residency, privacy laws, and industry-specific mandates.
• Linking 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 defining acceptable usage boundaries.
• Logging policies for prompts, responses, and metadata.
• Best practices for data retention, redaction, masking, and access control.
• Exercise: Analyzing an AI data flow to identify governance gaps.

Model Hosting and Inference Environment Strategies

• Evaluating deployment options: public APIs, private clouds, on-premise, and hybrid setups.
• Key factors in deciding where models should operate.
• Weighing the trade-offs between 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 departments.
• Approval workflows for use cases, model updates, and operational changes.
• Expectations for auditability, monitoring, and incident response.
• Constructing a practical sovereign AI roadmap and outlining next steps.