Lucebra
Lucebra-Geschäft
IT & Software/Netzwerk & Sicherheit/Generative KI (GenAI)

Securing GenAI Systems: Cloud, SDLC & Regulatory Compliance

Common Pitfalls, Advanced Threat Mitigation, GDPR & CCPA Ethics, Architecture Design | Final Project Included

Alle Stufen • 16 Vorträge • Lebenslanger Zugriff
Dr. Amar Massoud

Verifizierter Meister-Dozent

Securing GenAI Systems: Cloud, SDLC & Regulatory Compliance
ANHÄNGER
Treffen Sie Ihren Lehrer: Dr. Amar Massoud

19 ms

5.0 / 5.0
Globale Bewertung
1
Aktive Studenten
Englisch (Vereinigte Staaten)
Untertitel in 99 Sprachen
1 Std. 28 Min.
Gesamtinhalt

Was Sie lernen werden

Apply security best practices to protect Generative AI systems.
Identify and mitigate security gaps in AI deployments.
Design secure architectures for scalable and robust GenAI solutions.
Develop compliance frameworks aligned with GDPR, CCPA, and other regulations.

Kursinhalt

10 Abschnitte • 16 Vorträge • 01:28:33 Gesamtlänge

Introduction

05:18

Our Use Case - SecureAI Solutions Inc

04:03

Explore best practices for securing generative AI systems through practical cases at Secure AI Solutions, covering data protection, API security, regulatory compliance, and defending against adversarial attacks.

Foundations of GenAI Security (1)

06:43

Foundations of GenAI Security (2)

05:00

Apply access control and authentication to generative AI systems, enforcing least privilege and role-based access, with multi-factor authentication and IAM tools from AWS and Google Cloud, monitor via audit logs.

Securing GenAI in Cloud Environments (1)

06:26

Learn how cloud platforms secure generative AI with Google Safe and AWS generative AI Scoping Matrix, implement native security tools, and enforce identity, access control, monitoring, encryption, and key management.

Securing GenAI in Cloud Environments (2)

06:47
Wöchentlicher Lernplan
Planen Sie Ihre Lernreise
KI erstellt einen personalisierten Zeitplan basierend auf Ihrer Verfügbarkeit

Anforderungen

A basic understanding of Generative AI concepts is helpful but not required.

No prior cybersecurity experience is needed; all topics are explained in detail.

A willingness to learn and apply security principles in real-world scenarios.

Zielgruppe

AI engineers and developers looking to secure their AI systems.

Data scientists managing sensitive data in GenAI workflows.

Cybersecurity professionals expanding their expertise into AI security.

Tech leaders and managers responsible for secure AI deployments.

Über diesen Kurs

In a world where Generative AI systems are becoming integral to businesses, ensuring their security is more critical than ever. This comprehensive course is designed to equip you with the tools and knowledge to protect Generative AI (GenAI) systems effectively, balancing security, performance, and usability.

Learn how to safeguard sensitive data, defend against adversarial attacks, and secure APIs in cloud and on-premises environments. Explore industry best practices for securing AI development pipelines, managing compliance with regulations like GDPR and CCPA, and addressing ethical concerns such as bias and fairness. Dive deep into frameworks like Google SAIF and AWS Generative AI Scoping Matrix, and learn how to leverage cloud-native security tools to fortify your systems.

This course combines theory with hands-on exercises to give you practical experience. You’ll design secure architectures, configure access control policies, mitigate security gaps, and develop incident response plans tailored to GenAI systems. With our model company examples and templates, you’ll see exactly how these principles apply in real-world scenarios.

By the end of this course, you’ll be equipped to identify vulnerabilities, implement robust defenses, and ensure compliance while maintaining a seamless user experience. Whether you’re a data scientist, AI engineer, or security professional, this course will empower you to tackle the unique challenges of Generative AI security.

Take the next step in your career and build secure, scalable, and trustworthy GenAI systems. Enroll today to future-proof your AI expertise!

Karrierechancen & Zielpositionen

Entwickeln Sie praxisnahe, arbeitsmarktrelevante Fähigkeiten für gefragte Positionen

Praxisorientierter Lehrplan
Zielpositionen, für die Sie sich qualifizieren

Full Stack Software Engineer

Backend Systems Developer

Frontend Application Specialist

Automation & Scripting Engineer

Geschätztes Jahresgehalt
$90,000 – $145,000 / yr
Globale Branchennachfrage

High Global Tech Demand

Gefragte Kernkompetenzen
Production Code Architecture
RESTful & Modern API Design
State Management & UI Scaling
Debugging & Automated Testing
* Schätzungen basieren auf weltweiten Branchenberichten und Vergütungsstandards.
Lernen Sie Ihren Dozenten kennen
Dr. Amar Massoud
5.0 Bewertung
5 Studenten
Vollständiges Profil anzeigen
Dr. Amar Massoud
Hauptlehrer
PhD in computer science and IT manager with 36 years technical experience in various fields including IT Security, IT Governance, IT Service Management , Software Development, Project Management, Business Analysis and Software Architecture. I hold 80+ IT certifications such as : ITIL 4 Master, ITIL 3 Expert ISO 27001 Auditor, ComptIA Security+, GSEC, CEH, ECSA, CISM, CISSP, CISA PGMP, MSP PMP, PMI-ACP, Prince2 Practitioner, Praxis, Scrum Master COBIT 2019 Implementor, COBIT 5 Assessor/Implementer TOGAF certified Lean Specialist, VSM Specialist PMI RMP, ISO 31000 Risk Manager, ISO 22301 Lead Auditor PMI-PBA, CBAP Lean Six Sigma Black Belt, ISO 9001 Implementer Azure Administrator, Azure DevOps Expert, AWS Practitioner And many more....

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