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Course Outline

Understanding AI-Specific Risks in Government Settings

  • How AI risks differ from traditional IT and data risks.
  • Categories of AI risk: technical, operational, reputational, and ethical.
  • Public accountability and risk perception in government.

AI Risk Management Frameworks

  • NIST AI Risk Management Framework (AI RMF).
  • ISO/IEC 42001:2023 — AI Management System Standard.
  • Other sector-specific and international guidance (e.g., OECD, UNESCO).

Security Threats to AI Systems

  • Adversarial inputs, data poisoning, and model inversion.
  • Exposure of sensitive training data.
  • Supply chain and third-party model risks.

Governance, Auditing, and Controls

  • Human-in-the-loop mechanisms and accountability.
  • Auditable AI: documentation, versioning, and interpretability.
  • Internal controls, oversight roles, and compliance checkpoints.

Risk Assessment and Mitigation Planning

  • Building risk registers for AI use cases.
  • Collaborating with procurement, legal, and service design teams.
  • Conducting pre-deployment and post-deployment evaluations.

Incident Response and Public-Sector Resilience

  • Responding to AI-related incidents and breaches.
  • Communicating with stakeholders and the public.
  • Embedding AI risk practices in cybersecurity playbooks.

Summary and Next Steps

Requirements

  • Experience in IT operations, risk management, cybersecurity, or compliance within government institutions.
  • Familiarity with organizational security practices and digital service delivery.
  • No prior technical expertise in AI systems is required.

Target Audience

  • Government IT teams responsible for digital services and systems integration.
  • Cybersecurity and risk professionals working in public institutions.
  • Personnel involved in public sector audit, compliance, and governance.
 7 Hours

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