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

Foundations of Sovereign AI

• The meaning and implications of sovereign AI in regulated organizations
• Key business, legal, and operational motivators
• Primary control domains: data, models, infrastructure, and operations

Regulatory Compliance and Risk Assessment

• Requirements for data residency, privacy, and sector-specific rules
• Aligning sensitive data with specific AI use cases
• Identifying risks involving cross-border data, logging, and third-party exposure

Governing Data, Prompts, and Logs

• Establishing prompt governance and defining acceptable use limits
• Formulating logging policies for prompts, responses, and metadata
• Implementing retention, redaction, masking, and access control protocols
• Activity: Analyzing an AI data flow to identify governance shortcomings

Options for Model Hosting and Inference Environments

• Comparing deployment models: Public API, private cloud, on-premise, and hybrid
• Key considerations for determining model execution locations
• Balancing trade-offs among control, security, cost, and operational ownership

Mitigating Vendor Dependence and Enhancing Portability

• Recognizing common lock-in patterns across models, tools, and platforms
• Achieving portability via modular architecture, open interfaces, and clear contractual terms
• Activity: Assessing a vendor based on sovereignty metrics

Governance Framework and Action Plan

• Defining roles and responsibilities across IT, security, legal, and compliance teams
• Setting up approval workflows for use cases, models, and operational changes
• Establishing expectations for auditability, monitoring, and incident response
• Developing a practical sovereign AI roadmap and immediate next steps