Who is the target audience for this course?

The course is designed for data scientists, AI engineers, cybersecurity professionals, and students who want to specialize in securing AI and machine learning systems.

Do I need prior experience in AI or cybersecurity?

You should be comfortable with Python and familiar with basic machine learning concepts. Some cybersecurity knowledge is helpful but not required.

What practical skills will I gain from this course?

You will learn to detect vulnerabilities in AI pipelines, defend against adversarial attacks, secure deployment environments, and apply governance standards.

How is this course different from general cybersecurity training?

This program focuses specifically on threats and defenses unique to AI and machine learning, making it highly relevant for modern AI-driven industries.

Will I work with real-world datasets in this course?

Yes, you will complete hands-on labs and projects using realistic datasets that simulate industry scenarios.

Can this course help me advance my career?

Absolutely. Skills in AI security are in high demand. Completing this course prepares you for roles such as AI Security Engineer, Machine Learning Engineer with a focus on safety, or Cybersecurity Specialist working with AI solutions.

What industries can benefit from applying these skills?

Industries such as healthcare, finance, defense, manufacturing, and technology can all benefit from AI security practices taught in this course.

When will I have access to the lectures and assignments?

To access the course materials, assignments and to earn a Certificate, you will need to purchase the Certificate experience when you enroll in a course. You can try a Free Trial instead, or apply for Financial Aid. The course may offer 'Full Course, No Certificate' instead. This option lets you see all course materials, submit required assessments, and get a final grade. This also means that you will not be able to purchase a Certificate experience.

What will I get if I subscribe to this Specialization?

When you enroll in the course, you get access to all of the courses in the Specialization, and you earn a certificate when you complete the work. Your electronic Certificate will be added to your Accomplishments page - from there, you can print your Certificate or add it to your LinkedIn profile.

Is financial aid available?

Yes. In select learning programs, you can apply for financial aid or a scholarship if you can’t afford the enrollment fee. If fin aid or scholarship is available for your learning program selection, you’ll find a link to apply on the description page.

Securing AI Systems

本课程是 AI Security 专项课程的一部分

位教师：Edureka

包含在中

了解更多

4个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

1 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

4个模块

深入了解一个主题并学习基础知识。

中级等级

推荐体验

1 周完成

在 10 小时一周

灵活的计划

自行安排学习进度

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

您将学习的工具

Model Deployment

要了解的详细信息

可分享的证书

添加到您的领英档案

作业

12 任务¹

AI 评分请参见免责声明

授课语言：英语（English）

91% of learners achieved a positive career outcome

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Petrobras, TATA, Danone, Capgemini, P&G 和 L'Oreal 的徽标

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

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.

By the end of the course, you will be able to secure AI pipelines, strengthen deployment environments, and implement monitoring and governance frameworks that ensure responsible AI use. This course is ideal for AI engineers, data scientists, cybersecurity professionals, and students aspiring to specialize in AI security. While prior knowledge of Python and basic machine learning concepts is recommended, all core security techniques will be taught step by step. Do not just build smarter AI. Build safer AI. Enroll now to gain the expertise needed to protect tomorrow’s intelligent systems,

单元详情

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

10个视频总计52分钟

Specialization Introduction7分钟
Course Introduction5分钟
Adversarial Training4分钟
Defensive Distillation4分钟
Real-time Defense Against Adversarial Attacks5分钟
Demonstration: Adversarial Training for Robust Classification5分钟
Red Teaming for AI Security Testing5分钟
Rules of Engagement and Safety Controls5分钟
Attack Surface Mapping and Kill Chain Design5分钟
Demonstration: Red-Teaming Framework for AI Security Testing7分钟

4篇阅读材料总计40分钟

Course Overview10分钟
Federated Learning and Privacy-Preserving AI10分钟
AI Red Teaming Playbook: Simulating Attacks for Risk Discovery10分钟
Module Summary: Designing Resilient AI Models10分钟

3个作业总计42分钟

Knowledge Check: Designing Resilient AI Models30分钟
Practice Quiz: Adversarial Defense Techniques6分钟
Practice Quiz: Red Teaming AI Systems6分钟

2个讨论话题总计10分钟

Prioritizing Adversarial Defense5分钟
Simulating Real-World Attacks on AI Systems5分钟

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

14个视频总计73分钟

Integrating AI in SIEM and SOAR Tools5分钟
AI-Driven Threat Intelligence and Analysis5分钟
Automating Response with AI Playbooks5分钟
AI for Reconnaissance Detection and OSINT Defense6分钟
AI in Mitigating DoS and DDoS Attacks6分钟
Types of DoS and DDoS Attacks7分钟
Demonstration: Using theHarvester on a Social Networking Site4分钟
Demonstration: Demonstrating DoS Attacks Using hping34分钟
Demonstration: Verifying an Ongoing DoS/DDoS Using Wireshark3分钟
Incident Response Runbooks for AI5分钟
Containment and Eradication Procedures4分钟
Demonstration: Investigating Model and Data Compromise7分钟
Validation, Recovery and Return-to-Service5分钟
Demonstration: Containing Prompt Injection and Model Abuse7分钟

7篇阅读材料总计70分钟

AI-Augmented Threat Hunting and Incident Response Strategies10分钟
Cloud Security for AI: Securing Multi-Tenant Environments10分钟
OSINT with theHarvester: Techniques and Ethics10分钟
hping3 Traffic Crafting and Rate-Limiting Tests10分钟
Wireshark for DoS/DDoS Verification and PCAP Analysis10分钟
Forensic Readiness for AI: Logs, Artifacts, Chain-of-Custody10分钟
Module Summary: Advanced Threat Detection and Response10分钟

4个作业总计48分钟

Knowledge Check: Advanced Threat Detection and Response30分钟
Practice Quiz: AI in Security Operations Centers (SOCs)6分钟
Practice Quiz: Reconnaissance and DoS in Practice6分钟
Practice Quiz: Incident Response for AI6分钟

2个讨论话题总计15分钟

Accelerating Incident Response with AI5分钟
Choosing the Right Containment Strategy10分钟

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

9个视频总计53分钟

MLOps in Cybersecurity7分钟
Securing AI Workloads in the Cloud5分钟
Cloud AI Security Best Practices5分钟
Monitoring Cloud AI Deployments6分钟
Cloud IAM and Access Controls for AI Services6分钟
Hardware Attack Surface6分钟
Side-Channels and Co-Residency6分钟
Hardening and Mitigations6分钟
Demonstration: Hardening AI Workloads Against Hardware Side-Channels7分钟

4篇阅读材料总计40分钟

AI for Serverless Security: Cloud-Native Security Strategies10分钟
GPU/TPU Vulnerabilities: DMA, VRAM Leakage, Driver/IOMMU Controls10分钟
Side-Channel Detection and Noise-Injection Countermeasures10分钟
Module Summary: Secure MLOps and Deployment10分钟

3个作业总计42分钟

Knowledge Check: Secure MLOps and Deployment30分钟
Practice Quiz: Securing AI in the Cloud6分钟
Practice Quiz: Hardware Security for AI6分钟

2个讨论话题总计10分钟

Cloud Security Roadblocks for AI Systems5分钟
Mitigating Co-Residency Threats through Isolation5分钟

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.