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Securing AI Systems is a hands-on course designed to help you safeguard machine learning applications against real-world threats. You will explore vulnerabilities such as adversarial attacks, data poisoning, and model theft, and then practice defense strategies through guided labs.
Securing AI Systems
您将学到什么
Identify AI security concepts, attack types, and mitigation strategies.
Implement defenses, red-team simulations, and SOC/cloud/hardware security measures.
Evaluate weaknesses, assess defense effectiveness, and review incident response.
Design end-to-end secure AI systems and integrated security workflows.
您将获得的技能
- Application Security
- Threat Detection
- Artificial Intelligence and Machine Learning (AI/ML)
- Artificial Intelligence
- Machine Learning
- Responsible AI
- Vulnerability Assessments
- Threat Modeling
- Incident Response
- MLOps (Machine Learning Operations)
- Continuous Monitoring
- Information Systems Security
- Cloud Security
- Security Engineering
- Hardening
- Penetration Testing
- Security Controls
- Identity and Access Management
- Security Strategy
- Cybersecurity
要了解的详细信息
了解顶级公司的员工如何掌握热门技能
积累特定领域的专业知识
本课程是 AI Security 专项课程 专项课程的一部分
在注册此课程时，您还会同时注册此专项课程。
- 向行业专家学习新概念
- 获得对主题或工具的基础理解
- 通过实践项目培养工作相关技能
- 获得可共享的职业证书
该课程共有4个模块
Build robust AI systems by exploring adversarial defense techniques and red-teaming practices. Learn how models can be deceived by adversarial inputs, uncover vulnerabilities through simulated attacks, and apply strategies to harden models against manipulation. Gain hands-on experience in testing AI resilience and ensuring your models can withstand real-world threats.
涵盖的内容
10个视频4篇阅读材料3个作业2个讨论话题1个插件
Leverage AI-driven SOC tools to detect and respond to advanced cyber threats. Explore reconnaissance and DoS attack scenarios, understand how attackers infiltrate systems, and practice mitigation strategies that stop incidents before they escalate. Automate detection and response workflows to accelerate containment and strengthen your organization’s defense posture.
涵盖的内容
14个视频7篇阅读材料4个作业2个讨论话题
Strengthen the deployment of AI across cloud, edge, and multi-tenant environments. Learn to apply IAM controls, monitoring, and compliance safeguards to protect production pipelines. Develop strategies for secure scaling, ensuring your AI systems remain reliable, compliant, and resilient against both infrastructure-level and model-specific threats.
涵盖的内容
9个视频4篇阅读材料3个作业2个讨论话题
This module is designed to assess an individual on the various concepts and teachings covered in this course. Evaluate your knowledge with a comprehensive graded quiz.
涵盖的内容
1个视频1篇阅读材料2个作业1个讨论话题1个插件
获得职业证书
将此证书添加到您的 LinkedIn 个人资料、简历或履历中。在社交媒体和绩效考核中分享。
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常见问题
The course is designed for data scientists, AI engineers, cybersecurity professionals, and students who want to specialize in securing AI and machine learning systems.
You should be comfortable with Python and familiar with basic machine learning concepts. Some cybersecurity knowledge is helpful but not required.
You will learn to detect vulnerabilities in AI pipelines, defend against adversarial attacks, secure deployment environments, and apply governance standards.
更多问题
提供助学金，
¹ 本课程的部分作业采用 AI 评分。对于这些作业，将根据 Coursera 隐私声明使用您的数据。