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

Basics of Sovereign AI

• Interpreting sovereign AI within regulated organizations.
• Business, legal, and operational motivations.
• Primary control domains: data, models, infrastructure, and operations.

Regulatory Obligations and Risk Assessment

• Data residency, privacy laws, and industry-specific duties.
• Aligning sensitive data with AI applications.
• Recognizing risks related to cross-border transfers, logging, and third-party exposure.

Managing Data, Prompts, and Logs

• Governing prompts and setting acceptable use limits.
• Logging protocols for prompts, responses, and metadata.
• Best practices for retention, redaction, masking, and access control.
• Exercise: Auditing an AI data flow to identify governance deficiencies.

Model Hosting and Inference Environment Alternatives

• Comparison of public API, private cloud, on-premise, and hybrid deployment options.
• Key considerations for determining model execution locations.
• Balancing control, security, costs, and operational ownership.

Reducing Vendor Dependence and Enhancing Portability

• Typical lock-in scenarios in models, tools, and platforms.
• Achieving portability via modular design, open interfaces, and precise contracts.
• Exercise: Assessing a vendor against sovereignty standards.

Governance Framework and Strategic Planning

• Defining roles and responsibilities across IT, security, legal, and compliance.
• Workflows for approving use cases, models, and operational adjustments.
• Standards for auditability, monitoring, and incident response.
• Creating a practical sovereign AI roadmap and identifying next steps.