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Course Outline
Understanding AI-Specific Risk in Government Settings
- Distinctions between AI risk and traditional IT and data risk
- 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 structures
- Auditable AI: documentation, versioning, and interpretability
- Internal controls, oversight roles, and compliance checkpoints
Risk Assessment and Mitigation Planning
- Developing 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
- Integrating AI risk practices into 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
Audience
- Government IT teams engaged in digital services and systems integration
- Cybersecurity and risk professionals working in public institutions
- Personnel involved in public sector audit, compliance, and governance
7 Hours
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.
