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

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Securing AI Systems

Dieser Kurs ist Teil von Spezialisierung „AI Security“

Dozent: Edureka

Bei enthalten

Mehr erfahren

4 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

Stufe Mittel

Empfohlene Erfahrung

1 Woche zu vervollständigen

unter 10 Stunden pro Woche

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

4 Module

Verschaffen Sie sich einen Einblick in ein Thema und lernen Sie die Grundlagen.

Stufe Mittel

Empfohlene Erfahrung

1 Woche zu vervollständigen

unter 10 Stunden pro Woche

Flexibler Zeitplan

In Ihrem eigenen Lerntempo lernen

Was Sie lernen werden

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.

Kompetenzen, die Sie erwerben

Kategorie: MLOps (Machine Learning Operations)
Kategorie: Security Strategy
Kategorie: Identity and Access Management
Kategorie: Hardening
Kategorie: Computer Security Incident Management
Kategorie: Threat Modeling
Kategorie: Cloud Security
Kategorie: DevSecOps
Kategorie: Vulnerability Assessments
Kategorie: Incident Response
Kategorie: AI Security
Kategorie: Threat Detection
Kategorie: Machine Learning
Kategorie: Security Testing
Kategorie: Responsible AI
Kategorie: Cybersecurity
Kategorie: Artificial Intelligence
Kategorie: Penetration Testing
Kategorie: Anomaly Detection

Werkzeuge, die Sie lernen werden

Kategorie: Model Deployment

Wichtige Details

Zertifikat zur Vorlage

Zu Ihrem LinkedIn-Profil hinzufügen

Bewertungen

12 Zuweisungen¹

KI-bewertet siehe Haftungsausschluss

Unterrichtet in Englisch

Erfahren Sie, wie Mitarbeiter führender Unternehmen gefragte Kompetenzen erwerben.

Weitere Informationen zu Coursera für Unternehmen

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Erweitern Sie Ihre Fachkenntnisse

Dieser Kurs ist Teil der Spezialisierung Spezialisierung „AI Security“

Wenn Sie sich für diesen Kurs anmelden, werden Sie auch für diese Spezialisierung angemeldet.

Lernen Sie neue Konzepte von Branchenexperten
Gewinnen Sie ein Grundverständnis bestimmter Themen oder Tools
Erwerben Sie berufsrelevante Kompetenzen durch praktische Projekte
Erwerben Sie ein Berufszertifikat zur Vorlage

In diesem Kurs gibt es 4 Module

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,

Moduldetails

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.

Das ist alles enthalten

10 Videos4 Lektüren3 Aufgaben2 Diskussionsthemen

10 VideosInsgesamt 52 Minuten

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

4 LektürenInsgesamt 40 Minuten

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

3 AufgabenInsgesamt 42 Minuten

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

2 DiskussionsthemenInsgesamt 10 Minuten

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

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.

Das ist alles enthalten

14 Videos7 Lektüren4 Aufgaben2 Diskussionsthemen

14 VideosInsgesamt 73 Minuten

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

7 LektürenInsgesamt 70 Minuten

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

4 AufgabenInsgesamt 48 Minuten

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

2 DiskussionsthemenInsgesamt 15 Minuten

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

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.

Das ist alles enthalten

9 Videos4 Lektüren3 Aufgaben2 Diskussionsthemen

9 VideosInsgesamt 53 Minuten

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

4 LektürenInsgesamt 40 Minuten

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

3 AufgabenInsgesamt 42 Minuten

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

2 DiskussionsthemenInsgesamt 10 Minuten

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

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.