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 Duration 7 hours

Course Outline

Principles of Sovereign AI

  • Defining the scope and implications of sovereign AI within regulated entities
  • Strategic, statutory, and operational imperatives driving adoption
  • Primary domains of control: data assets, algorithmic models, infrastructure, and operations

Regulatory Compliance and Risk Assessment

  • Requirements for data residency, privacy protection, and industry-specific mandates
  • Correlating sensitive data sets with specific AI application scenarios
  • Identifying risks associated with cross-border transfers, audit logging, and third-party integrations

Oversight of Data, Prompts, and Logs

  • Establishing prompt governance frameworks and permissible use limits
  • Protocols for recording prompts, system responses, and associated metadata
  • Standardized practices for retention, redaction, masking, and access control
  • Practical application: auditing an AI data flow to identify governance deficiencies

Model Hosting and Inference Environment Strategies

  • Evaluation of deployment options: public APIs, private clouds, on-premises systems, and hybrid architectures
  • Key determinants for selecting the appropriate execution environment for models
  • Balancing trade-offs across control, security posture, cost efficiency, and operational accountability

Vendor Independence and System Portability

  • Identification of common lock-in mechanisms in models, tools, and platforms
  • Achieving portability through modular design, open standards, and explicit contractual terms
  • Practical application: assessing a vendor solution against sovereign criteria

Governance Architecture and Action Strategy

  • Defining roles and responsibilities across IT, security, legal, and compliance functions
  • Approval processes for use cases, model deployments, and operational modifications
  • Expectations for audit trails, continuous monitoring, and incident response
  • Developing a practical sovereign AI roadmap and immediate implementation steps

Requirements

  • Foundational knowledge of AI concepts, data governance, and compliance requirements
  • Working familiarity with enterprise technology, cloud infrastructure, security, or risk management decision-making
  • No programming experience is required

Target Audience

  • IT leaders, enterprise architects, and platform managers
  • Professionals in risk management, compliance, legal affairs, and data governance
  • Security teams and business executives responsible for AI integration within regulated environments

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