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Course Outline

Understanding AI-Specific Risks in Government Environments

  • Differences between AI risk and traditional IT/data risk
  • AI risk categories: technical, operational, reputational, and ethical
  • Public accountability and risk perception in government contexts

AI Risk Management Frameworks

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

Security Threats to AI Systems

  • Adversarial inputs, data poisoning, and model inversion attacks
  • Risks of sensitive training data exposure
  • Supply chain and third-party model risks

Governance, Auditing, and Controls

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

Risk Assessment and Mitigation Planning

  • Creating 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

  • Addressing AI-related incidents and breaches
  • Communicating effectively with stakeholders and the public
  • Integrating AI risk practices into cybersecurity playbooks

Summary and Next Steps

Requirements

  • Background in IT operations, risk management, cybersecurity, or compliance within government institutions
  • Understanding of organizational security practices and digital service delivery
  • No previous technical expertise in AI systems is required

Target Audience

  • Government IT teams responsible for digital services and system integration
  • Cybersecurity and risk professionals working in public institutions
  • Personnel handling audits, compliance, and governance in the public sector
 7 Hours

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