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.

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

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Securing AI Systems

This course is part of AI Security Specialization

Instructor: Edureka

Included with

Learn more

4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

4 modules

Gain insight into a topic and learn the fundamentals.

Intermediate level

Recommended experience

1 week to complete

at 10 hours a week

Flexible schedule

Learn at your own pace

What you'll learn

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.

Skills you'll gain

Tools you'll learn

Model Deployment

Details to know

Shareable certificate

Add to your LinkedIn profile

Assessments

12 assignments¹

AI Graded see disclaimer

Taught in English

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Build your subject-matter expertise

This course is part of the AI Security Specialization

When you enroll in this course, you'll also be enrolled in this Specialization.

Learn new concepts from industry experts
Gain a foundational understanding of a subject or tool
Develop job-relevant skills with hands-on projects
Earn a shareable career certificate

There are 4 modules in this course

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,

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.

What's included

10 videos4 readings3 assignments2 discussion prompts

10 videosTotal 52 minutes

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

4 readingsTotal 40 minutes

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

3 assignmentsTotal 42 minutes

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

2 discussion promptsTotal 10 minutes

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

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.

What's included

14 videos7 readings4 assignments2 discussion prompts

14 videosTotal 73 minutes

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

7 readingsTotal 70 minutes

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

4 assignmentsTotal 48 minutes

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

2 discussion promptsTotal 15 minutes

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

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.

What's included

9 videos4 readings3 assignments2 discussion prompts

9 videosTotal 53 minutes

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

4 readingsTotal 40 minutes

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

3 assignmentsTotal 42 minutes

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

2 discussion promptsTotal 10 minutes

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

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.

What's included

1 video1 reading2 assignments1 discussion prompt

Earn a career certificate

Add this credential to your LinkedIn profile, resume, or CV. Share it on social media and in your performance review.

Instructor

Edureka

175 Courses156,958 learners

Offered by

Edureka

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Frequently asked questions

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.

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.