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Duration 7 hours
Course Outline
Grasping AI-Specific Risk in Government Environments
- Distinguishing AI risk from traditional IT and data risks.
- Categorizing AI risks: technical, operational, reputational, and ethical.
- Understanding 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 Facing AI Systems
- Adversarial inputs, data poisoning, and model inversion.
- 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 Strategies
- 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
- Responding to AI-related incidents and breaches.
- Communicating with stakeholders and the general public.
- Integrating AI risk practices into cybersecurity playbooks.
Summary and Next Steps
Requirements
- Experience in IT operations, risk management, cybersecurity, or compliance within government institutions.
- No prior technical expertise in AI systems is required.
Target Audience
- Government IT teams engaged in digital services and systems integration.
- Cybersecurity and risk professionals working in public institutions.
- Public sector personnel involved in audit, compliance, and governance.
Testimonials (1)
I got to learn more about Microsoft Copilot, something that I thought was the same as chatGPT but I got to discover more exciting options that I will forever use to make my life easy.
