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Duration 7 hours
Course Outline
Understanding AI-Specific Risks in Government Contexts
- Distinguishing AI risks from traditional IT and data risks.
- Categorizing AI risks: technical, operational, reputational, and ethical.
- Navigating public accountability and risk perception within government.
AI Risk Management Frameworks
- The NIST AI Risk Management Framework (AI RMF).
- ISO/IEC 42001:2023 — The AI Management System Standard.
- Other sector-specific and international guidelines (e.g., OECD, UNESCO).
Security Threats to AI Systems
- Addressing adversarial inputs, data poisoning, and model inversion.
- Protecting sensitive training data from exposure.
- Managing supply chain and third-party model risks.
Governance, Auditing, and Controls
- Implementing human-in-the-loop processes and accountability mechanisms.
- Ensuring auditable AI through documentation, versioning, and interpretability.
- Establishing internal controls, oversight roles, and compliance checkpoints.
Risk Assessment and Mitigation Planning
- Creating risk registers for specific 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 effectively to AI-related incidents and breaches.
- Communicating with stakeholders and the general public.
- Integrating AI risk practices into cybersecurity playbooks.
Summary and Recommended Next Steps
Requirements
- Background in IT operations, risk management, cybersecurity, or compliance within government organizations.
- Knowledge of organizational security practices and digital service delivery.
- No prior technical expertise in AI systems is necessary.
Target Audience
- Government IT teams engaged in digital services and systems integration.
- Cybersecurity and risk professionals working in public institutions.
- Public sector personnel responsible for audit, compliance, and governance.
Testimonials (1)
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