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Course Outline

Foundations of Sovereign AI for Government

  • Understanding the concept of sovereign AI in regulated organizations for government
  • Key business, legal, and operational drivers influencing sovereign AI adoption for government
  • Core control areas: data management, model development, infrastructure, and operations for government

Regulatory Requirements and Risk Mapping for Government

  • Data residency, privacy regulations, and sector-specific obligations for government
  • Mapping sensitive data to AI use cases in a governmental context
  • Identifying cross-border, logging, and third-party exposure risks for government operations

Governing Data, Prompts, and Logs for Government

  • Establishing prompt governance and defining acceptable use boundaries for government AI systems
  • Implementing logging policies for prompts, responses, and metadata in governmental applications
  • Practices for retention, redaction, masking, and access control in a governmental setting
  • Exercise: reviewing an AI data flow to identify governance gaps for government

Model Hosting and Inference Environment Options for Government

  • Evaluating public API, private cloud, on-premise, and hybrid deployment options for government AI models
  • Criteria for determining where models should run in a governmental context
  • Assessing trade-offs among control, security, cost, and operational ownership for government operations

Vendor Dependence and Portability for Government

  • Identifying common lock-in patterns in models, tools, and platforms for government AI projects
  • Ensuring portability through modular architecture, open interfaces, and clear contracts for government systems
  • Exercise: evaluating a vendor against sovereignty criteria for government use

Governance Model and Action Planning for Government

  • Defining roles and responsibilities across IT, security, legal, and compliance functions in government
  • Establishing approval workflows for AI use cases, models, and operational changes in a governmental setting
  • Setting expectations for auditability, monitoring, and incident response in government AI initiatives
  • Developing a practical sovereign AI roadmap and next steps for government agencies

Requirements

  • A foundational knowledge of artificial intelligence (AI) concepts, data governance, and regulatory compliance requirements.
  • Familiarity with enterprise technology, cloud services, security protocols, or risk management decision-making processes.
  • No prior programming experience is necessary.

Intended Audience

  • IT leaders, enterprise architects, and platform managers for government and private sector organizations.
  • Risk, compliance, legal, and data governance professionals within public and private entities.
  • Security teams and business leaders responsible for the adoption of AI in regulated environments, including those for government agencies.
 7 Hours

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