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Course Outline

Principles of Sovereign Artificial Intelligence

  • Defining sovereign AI within highly regulated enterprises
  • Business, legal, and operational imperatives driving adoption
  • Core control domains: data management, model integrity, infrastructure, and operational oversight

Regulatory Compliance and Risk Assessment

  • Data residency mandates, privacy protections, and industry-specific obligations
  • Aligning sensitive information with designated AI applications
  • Identifying risks associated with cross-border data transfers, logging requirements, and third-party dependencies for government entities

Data, Prompt, and Log Governance

  • Prompt management protocols and acceptable use parameters
  • Policies governing the capture of prompts, model responses, and metadata
  • Standards for data retention, redaction, masking, and access controls
  • Exercise: evaluating AI data flows to identify governance deficiencies

Model Hosting and Inference Deployment Strategies

  • Deployment models: public APIs, private cloud environments, on-premise solutions, and hybrid configurations
  • Key determinants for selecting appropriate model execution environments
  • Evaluating trade-offs among security posture, cost efficiency, operational control, and ownership responsibilities

Vendor Reliance and Interoperability

  • Prevalent patterns of vendor lock-in across models, tools, and platforms
  • Achieving portability through modular design, open standards, and explicit contractual terms
  • Exercise: assessing vendors against sovereignty and compliance criteria

Governance Framework and Strategic Planning

  • Defining roles and responsibilities across IT, security, legal, and compliance functions
  • Establishing approval workflows for use cases, model deployments, and operational modifications
  • Requirements for auditability, continuous monitoring, and incident response protocols
  • Developing a practical sovereign AI implementation roadmap and subsequent actions

Requirements

  • Fundamental knowledge of artificial intelligence principles, data stewardship frameworks, and regulatory obligations
  • Experience evaluating enterprise technology infrastructure, cloud solutions, security protocols, or risk management strategies
  • Technical coding skills are not necessary

Intended Audience

  • Information technology executives, enterprise architects, and platform governance officials
  • Specialists in regulatory compliance, legal affairs, and data governance
  • Security personnel and executive leaders tasked with implementing AI initiatives within regulated sectors for government purposes
 7 Hours

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