- 具备机器学习和AI概念的基础知识
- 熟悉网络安全原则和实践
受众
- 希望提升AI系统安全性的AI和机器学习工程师
- 专注于AI模型保护的网络安全专业人士
- 数据治理和安全性领域的合规与风险管理专业人士
保护AI系统带来了与传统网络安全方法不同的独特挑战。AI系统容易受到对抗性攻击、数据投毒和模型窃取的威胁，这些都会严重影响业务运营和数据完整性。本课程探讨了AI系统的主要网络安全实践，涵盖对抗性机器学习、机器学习管道中的数据安全性以及稳健AI部署的合规要求。
本次由讲师指导的培训（线上或线下）面向中级AI和网络安全专业人员，旨在帮助学员理解和解决AI模型和系统特有的安全漏洞，特别是在金融、数据治理和咨询等高度监管的行业中。
通过本次培训，学员将能够：
- 了解针对AI系统的对抗性攻击类型及其防御方法。
- 实施模型硬化技术以保护机器学习管道。
- 确保机器学习模型中的数据安全性和完整性。
- 掌握与AI安全相关的法规合规要求。
课程形式
- 互动式讲座与讨论。
- 大量练习与实践。
- 在实时实验室环境中进行实际操作。
课程定制选项
- 如需为本课程定制培训，请联系我们进行安排。
AI Security 挑战简介
- 了解AI系统特有的安全风险
- 传统网络安全与AI网络安全的比较
- AI模型攻击面的概述
对抗性Machine Learning
- 对抗性攻击的类型：规避、毒化和提取
- 实施对抗性防御和应对措施
- 不同行业中的对抗性攻击案例研究
模型加固技术
- 模型鲁棒性和加固简介
- 减少模型对攻击的脆弱性的技术
- 实践防御性蒸馏和其他加固方法
Machine Learning中的数据安全
- 确保训练和推理数据管道的安全性
- 防止数据泄漏和模型反演攻击
- 在AI系统中管理敏感数据的最佳实践
AI Security合规性和法规要求
- 了解与AI和数据安全相关的法规
- 遵守GDPR、CCPA和其他数据保护法律
- 开发安全且合规的AI模型
AI系统安全的监控与维护
- 实施AI系统的持续监控
- 机器学习的日志记录和审计
- 应对AI安全事件和违规行为
AI网络安全的未来趋势
- 保护AI和机器学习的新兴技术
- AI网络安全中的创新机会
- 为未来的AI安全挑战做好准备
总结与下一步
United Arab Emirates - Cybersecurity in AI Systems
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Saudi Arabia - Cybersecurity in AI Systems
South Africa - Cybersecurity in AI Systems
Brasil - Cybersecurity in AI Systems
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中国 - Cybersecurity in AI Systems
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USA - Cybersecurity in AI Systems
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Schweiz - Cybersecurity in AI Systems
Deutschland - Cybersecurity in AI Systems
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Greece - Cybersecurity in AI Systems
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Slovenia - Cybersecurity in AI Systems
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Serbia - Cybersecurity in AI Systems
Bhutan - Cybersecurity in AI Systems