Securing AI Systems

This course is part of AI Security Specialization

Instructor: Edureka

Access provided by Tata Steel Learning and Development

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 videos Total 52 minutes

Specialization Introduction 7 minutes
Course Introduction 5 minutes
Adversarial Training 4 minutes
Defensive Distillation 4 minutes
Real-time Defense Against Adversarial Attacks 5 minutes
Demonstration: Adversarial Training for Robust Classification 5 minutes
Red Teaming for AI Security Testing 5 minutes
Rules of Engagement and Safety Controls 5 minutes
Attack Surface Mapping and Kill Chain Design 5 minutes
Demonstration: Red-Teaming Framework for AI Security Testing 7 minutes

4 readings Total 40 minutes

Course Overview 10 minutes
Federated Learning and Privacy-Preserving AI 10 minutes
AI Red Teaming Playbook: Simulating Attacks for Risk Discovery 10 minutes
Module Summary: Designing Resilient AI Models 10 minutes

3 assignments Total 42 minutes

Knowledge Check: Designing Resilient AI Models 30 minutes
Practice Quiz: Adversarial Defense Techniques 6 minutes
Practice Quiz: Red Teaming AI Systems 6 minutes

2 discussion prompts Total 10 minutes

Prioritizing Adversarial Defense 5 minutes
Simulating Real-World Attacks on AI Systems 5 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 videos Total 73 minutes

Integrating AI in SIEM and SOAR Tools 5 minutes
AI-Driven Threat Intelligence and Analysis 5 minutes
Automating Response with AI Playbooks 5 minutes
AI for Reconnaissance Detection and OSINT Defense 6 minutes
AI in Mitigating DoS and DDoS Attacks 6 minutes
Types of DoS and DDoS Attacks 7 minutes
Demonstration: Using theHarvester on a Social Networking Site 4 minutes
Demonstration: Demonstrating DoS Attacks Using hping3 4 minutes
Demonstration: Verifying an Ongoing DoS/DDoS Using Wireshark 3 minutes
Incident Response Runbooks for AI 5 minutes
Containment and Eradication Procedures 4 minutes
Demonstration: Investigating Model and Data Compromise 7 minutes
Validation, Recovery and Return-to-Service 5 minutes
Demonstration: Containing Prompt Injection and Model Abuse 7 minutes

7 readings Total 70 minutes

AI-Augmented Threat Hunting and Incident Response Strategies 10 minutes
Cloud Security for AI: Securing Multi-Tenant Environments 10 minutes
OSINT with theHarvester: Techniques and Ethics 10 minutes
hping3 Traffic Crafting and Rate-Limiting Tests 10 minutes
Wireshark for DoS/DDoS Verification and PCAP Analysis 10 minutes
Forensic Readiness for AI: Logs, Artifacts, Chain-of-Custody 10 minutes
Module Summary: Advanced Threat Detection and Response 10 minutes

4 assignments Total 48 minutes

Knowledge Check: Advanced Threat Detection and Response 30 minutes
Practice Quiz: AI in Security Operations Centers (SOCs) 6 minutes
Practice Quiz: Reconnaissance and DoS in Practice 6 minutes
Practice Quiz: Incident Response for AI 6 minutes

2 discussion prompts Total 15 minutes

Accelerating Incident Response with AI 5 minutes
Choosing the Right Containment Strategy 10 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 videos Total 53 minutes

MLOps in Cybersecurity 7 minutes
Securing AI Workloads in the Cloud 5 minutes
Cloud AI Security Best Practices 5 minutes
Monitoring Cloud AI Deployments 6 minutes
Cloud IAM and Access Controls for AI Services 6 minutes
Hardware Attack Surface 6 minutes
Side-Channels and Co-Residency 6 minutes
Hardening and Mitigations 6 minutes
Demonstration: Hardening AI Workloads Against Hardware Side-Channels 7 minutes

4 readings Total 40 minutes

AI for Serverless Security: Cloud-Native Security Strategies 10 minutes
GPU/TPU Vulnerabilities: DMA, VRAM Leakage, Driver/IOMMU Controls 10 minutes
Side-Channel Detection and Noise-Injection Countermeasures 10 minutes
Module Summary: Secure MLOps and Deployment 10 minutes

3 assignments Total 42 minutes

Knowledge Check: Secure MLOps and Deployment 30 minutes
Practice Quiz: Securing AI in the Cloud 6 minutes
Practice Quiz: Hardware Security for AI 6 minutes

2 discussion prompts Total 10 minutes

Cloud Security Roadblocks for AI Systems 5 minutes
Mitigating Co-Residency Threats through Isolation 5 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.