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
Securing AI Systems
This course is part of AI Security Specialization
Instructor: Edureka
Included with 
Gain insight into a topic and learn the fundamentals.
Intermediate level
Recommended experience
9 hours to complete
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
- Cybersecurity
- Incident Response
- Information Systems Security
- MLOps (Machine Learning Operations)
- Security Controls
- Continuous Monitoring
- Hardening
- Vulnerability Assessments
- Threat Modeling
- Cloud Security
- Artificial Intelligence and Machine Learning (AI/ML)
- Responsible AI
- Security Strategy
- Penetration Testing
- Application Security
- Artificial Intelligence
- Machine Learning
- Security Engineering
- Threat Detection
- Identity and Access Management
Details to know
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Recently updated!
October 2025
Taught in English
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This course is part of the AI Security Specialization
When you enroll in this course, you'll also be enrolled in this Specialization.
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- Gain a foundational understanding of a subject or tool
- Develop job-relevant skills with hands-on projects
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There are 4 modules in this course
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 prompts1 plugin
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
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
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 prompt1 plugin
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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.
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