Securing Generative AI Systems
Securing Generative AI Systems
Published 7/2024
Duration: 5h41m | .MP4 1280x720, 30 fps(r) | AAC, 44100 Hz, 2ch | 1.85 GB
Genre: eLearning | Language: English
Learn how to secure Generative AI Systems | Hands-on experience in your own free Azure environment
What you'll learn
Explore the fundamentals of generative AI, including its principles, applications, and implications for cyber security.
Familiarize yourself with essential Azure services and configurations.
Establish a strong foundation with an overview of core concepts for a Security Operations Center.
Learn the key concepts of Cyber Threat Intelligence.
Understand the essentials of identifying, prioritizing, and mitigating vulnerabilities within an organization's infrastructure.
Gain insight into Microsoft's security ecosystem, including tools, best practices, and zero trust for safeguarding digital assets.
Discover specific cybersecurity challenges and solutions tailored to generative AI systems.
Learn about common attack vectors targeting generative AI and effective countermeasures to defend against them.
Understand additional risks associated with generative AI, including ethical concerns, bias, and compliance issues.
Review the OWASP Top 10 vulnerabilities specific to large language models and how to mitigate them.
Get acquainted with the MITRE ATLAS framework and its application to generative AI threat analysis.
Develop skills in threat modeling to identify, assess, and address potential security threats to generative AI systems.
Design and implement a robust security architecture tailored for generative AI environments.
Analyze a detailed case study on exploiting large language models to understand potential vulnerabilities and protective measures.
Gain hands-on experience by building and securing your own generative AI model using Azure OpenAI Services
Requirements
Basic IT Knowledge
Willingness to learn cool stuff!
Description
Securing Generative AI Systems is a meticulously structured Udemy course aimed at IT professionals seeking to master the security of generative AI systems. This course systematically walks you through the initial basics to advanced concepts with applied case studies.
You will gain a deep understanding of the principles and practices necessary for effective security. The course combines theoretical knowledge with practical insights to ensure comprehensive learning. By the end of the course, you'll be equipped with the skills to implement security frameworks that align with your enterprise's strategic goals.
Key Benefits for you:
Generative AI Basics:
Explore the fundamentals of generative AI, including its principles, applications, and implications for cyber security.
Azure Basics:
Familiarize yourself with essential Azure services and configurations.
SOC Basics:
Establish a strong foundation with an overview of core concepts for a Security Operations Centers.
CTI Basics:
Learn the key concepts of Cyber Threat Intelligence.
Vulnerabilities Basics:
Understand the essentials of identifying, prioritizing, and mitigating vulnerabilities within an organization's infrastructure.
Microsoft Security Basics:
Gain insight into Microsoft's security ecosystem, including tools, best practices, and zero trust for safeguarding digital assets.
Cyber Security for GenAI:
Discover specific cybersecurity challenges and solutions tailored to generative AI systems.
Attacks and Countermeasures for GenAI:
Learn about common attack vectors targeting generative AI and effective countermeasures to defend against them.
Other Risks for GenAI:
Understand additional risks associated with generative AI, including ethical concerns, bias, and compliance issues.
OWASP Top 10 for Large Language Models (LLMs):
Review the OWASP Top 10 vulnerabilities specific to large language models and how to mitigate them.
MITRE ATLAS:
Get acquainted with the MITRE ATLAS framework and its application to generative AI threat analysis.
Threat Modeling:
Develop skills in threat modeling to identify, assess, and address potential security threats to generative AI systems.
A Security Architecture for Generative AI:
Design and implement a robust security architecture tailored for generative AI environments.
Case Study I - Exploit LLMs:
Analyze a detailed case study on exploiting large language models to understand potential vulnerabilities and protective measures.
Case Study II - Build and secure your own GPT with Azure OpenAI Services:
Gain hands-on experience by building and securing your own generative AI model using Azure OpenAI Services, applying the security principles and practices learned throughout the course in combination with Microsoft Defender CSPM.
Who this course is for:
SOC Analyst
Security Engineer
Security Consultant
Security Architect
Security Manager
CISO
Red Team
Blue Team
Cybersecurity Professional
Ethical Hacker
Penetration Tester
Incident Handler
IT Architect
Cloud Architect
More Info
What you'll learn
Explore the fundamentals of generative AI, including its principles, applications, and implications for cyber security.
Familiarize yourself with essential Azure services and configurations.
Establish a strong foundation with an overview of core concepts for a Security Operations Center.
Learn the key concepts of Cyber Threat Intelligence.
Understand the essentials of identifying, prioritizing, and mitigating vulnerabilities within an organization's infrastructure.
Gain insight into Microsoft's security ecosystem, including tools, best practices, and zero trust for safeguarding digital assets.
Discover specific cybersecurity challenges and solutions tailored to generative AI systems.
Learn about common attack vectors targeting generative AI and effective countermeasures to defend against them.
Understand additional risks associated with generative AI, including ethical concerns, bias, and compliance issues.
Review the OWASP Top 10 vulnerabilities specific to large language models and how to mitigate them.
Get acquainted with the MITRE ATLAS framework and its application to generative AI threat analysis.
Develop skills in threat modeling to identify, assess, and address potential security threats to generative AI systems.
Design and implement a robust security architecture tailored for generative AI environments.
Analyze a detailed case study on exploiting large language models to understand potential vulnerabilities and protective measures.
Gain hands-on experience by building and securing your own generative AI model using Azure OpenAI Services
Requirements
Basic IT Knowledge
Willingness to learn cool stuff!
Description
Securing Generative AI Systems is a meticulously structured Udemy course aimed at IT professionals seeking to master the security of generative AI systems. This course systematically walks you through the initial basics to advanced concepts with applied case studies.
You will gain a deep understanding of the principles and practices necessary for effective security. The course combines theoretical knowledge with practical insights to ensure comprehensive learning. By the end of the course, you'll be equipped with the skills to implement security frameworks that align with your enterprise's strategic goals.
Key Benefits for you:
Generative AI Basics:
Explore the fundamentals of generative AI, including its principles, applications, and implications for cyber security.
Azure Basics:
Familiarize yourself with essential Azure services and configurations.
SOC Basics:
Establish a strong foundation with an overview of core concepts for a Security Operations Centers.
CTI Basics:
Learn the key concepts of Cyber Threat Intelligence.
Vulnerabilities Basics:
Understand the essentials of identifying, prioritizing, and mitigating vulnerabilities within an organization's infrastructure.
Microsoft Security Basics:
Gain insight into Microsoft's security ecosystem, including tools, best practices, and zero trust for safeguarding digital assets.
Cyber Security for GenAI:
Discover specific cybersecurity challenges and solutions tailored to generative AI systems.
Attacks and Countermeasures for GenAI:
Learn about common attack vectors targeting generative AI and effective countermeasures to defend against them.
Other Risks for GenAI:
Understand additional risks associated with generative AI, including ethical concerns, bias, and compliance issues.
OWASP Top 10 for Large Language Models (LLMs):
Review the OWASP Top 10 vulnerabilities specific to large language models and how to mitigate them.
MITRE ATLAS:
Get acquainted with the MITRE ATLAS framework and its application to generative AI threat analysis.
Threat Modeling:
Develop skills in threat modeling to identify, assess, and address potential security threats to generative AI systems.
A Security Architecture for Generative AI:
Design and implement a robust security architecture tailored for generative AI environments.
Case Study I - Exploit LLMs:
Analyze a detailed case study on exploiting large language models to understand potential vulnerabilities and protective measures.
Case Study II - Build and secure your own GPT with Azure OpenAI Services:
Gain hands-on experience by building and securing your own generative AI model using Azure OpenAI Services, applying the security principles and practices learned throughout the course in combination with Microsoft Defender CSPM.
Who this course is for:
SOC Analyst
Security Engineer
Security Consultant
Security Architect
Security Manager
CISO
Red Team
Blue Team
Cybersecurity Professional
Ethical Hacker
Penetration Tester
Incident Handler
IT Architect
Cloud Architect
More Info
