- 在政府机构中具有IT运营、风险管理、网络安全或合规经验
- 熟悉组织安全实践和数字服务交付
- 无需具备AI系统的技术专业知识
受众
- Go涉及数字服务和系统集成的政府IT团队
- 公共机构中的网络安全和风险专业人员
- 公共部门审计、合规和治理人员
Artificial Intelligence (AI) 为政府机构和部门引入了新的运营风险维度、治理挑战和网络安全暴露。
本课程为讲师主导的培训（线上或线下），面向公共部门的IT和风险专业人士，他们此前对AI经验有限，但希望了解如何在政府或监管环境中评估、监控和保护AI系统。
在本培训结束时，参与者将能够：
- 解释与AI系统相关的关键风险概念，包括偏见、不可预测性和模型漂移。
- 应用AI特定的治理和审计框架，如NIST AI RMF和ISO/IEC 42001。
- 识别针对AI模型和数据管道的网络安全威胁。
- 建立跨部门的风险管理计划和政策对齐，以支持AI部署。
课程形式
- 互动讲座和公共部门用例讨论。
- AI治理框架练习和政策映射。
- 基于场景的威胁建模和风险评估。
课程定制选项
- 如需为本课程定制培训，请联系我们安排。
理解Go政府环境中的AI特定风险
- AI风险与传统IT和数据风险的区别
- AI风险的类别：技术、运营、声誉和伦理
- 政府中的公共问责和风险感知
AI Risk Management框架
- NIST AI Risk Management框架 (AI RMF)
- ISO/IEC 42001:2023 — AI Management系统标准
- 其他特定行业和国际指南（如OECD, UNESCO）
AI系统的安全威胁
- 对抗性输入、数据中毒和模型反转
- 敏感训练数据的暴露
- 供应链和第三方模型风险
Go治理、审计和控制
- 人机协作和问责机制
- 可审计的AI：文档、版本控制和可解释性
- 内部控制、监督角色和合规检查点
风险评估和缓解计划
- 为AI用例建立风险登记册
- 与采购、法律和服务设计团队合作
- 进行部署前和部署后的评估
事件响应和公共部门的韧性
- 应对AI相关事件和漏洞
- 与利益相关者和公众沟通
- 将AI风险实践嵌入网络安全应对手册
总结和下一步
United Arab Emirates - AI Risk Management and Security in the Public Sector
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