Generative Ai Security For Safe Use In Organizations
Generative Ai Security For Safe Use In Organizations
Published 6/2024
MP4 | Video: h264, 1920x1080 | Audio: AAC, 44.1 KHz
Language: English | Size: 866.72 MB | Duration: 2h 7m
Best Practices, Case Studies, Security Frameworks and 120 Examples
What you'll learn
Generative Al
Ethical Concerns and Bias in Al-Generated Content
Security Risks Associated with Generative Al
Data Poisoning and Prompt Injection Attacks
Model Inversion and Data Leakage
Model Stealing and Its Implications
Inadequate Sandboxing and Malicious Code Execution
Top Threats Presented by Public Al
Developing a Security Framework for Generative AI
120 Examples
Case Studies
Best Practices for Safe Use of Generative Al
Promoting a Culture of Security Awareness in Al
Requirements
Eager to learn Generative AI Security.
Description
This course provides a comprehensive understanding of Generative AI (GenAI) technologies, their applications across various industries, and the associated security and ethical considerations. Participants will gain insights into the types of Generative AI models, their potential vulnerabilities, and practical strategies for mitigating risks. Through detailed lectures, real-world examples, and case studies, this course aims to equip professionals with the knowledge and tools necessary to promote secure and ethical use of Generative AI.Security is a major concern in the deployment of Generative AI, and this course will delve into specific threats such as data poisoning, prompt injection attacks, model inversion, data leakage, and model stealing. Through detailed examples from different industries, participants will learn to identify and mitigate these risks. Ethical concerns, including bias in AI-generated content, will also be addressed, providing guidelines for ethical AI use.Participants will explore real-world case studies to understand the practical implications of these risks and learn to develop a comprehensive security framework tailored to their organizational needs. The course will emphasize best practices for the safe use of Generative AI, including the importance of sandboxing to prevent malicious code execution and promoting a culture of security awareness within organizations.By the end of the course, participants will be equipped with the knowledge and tools necessary to ensure the secure and ethical deployment of Generative AI technologies. Key Areas Covered Are:Applications of Generative Al in various industries-1Security Risks Associated with Generative AlEthical Concerns and Bias in Al-Generated ContentData Poisoning and Prompt Injection AttacksExample of Data Poisoning in different IndustriesExample of Prompt Injection Attacks in diferent IndustriesModel Inversion and Data LeakageExample of Model Inversion in different IndustriesExample of Data Leakage in different IndustriesModel Stealing and Its ImplicationsExample of Model Stealing in different IndustriesHallucinations in Generative AlInadequate Sandboxing and Malicious Code ExecutionExample of Inadequate Sandboxing in different IndustriesExample of Malicious Code in different IndustriesTop Threats Presented by Public Alcase studiescreating six steps security framework Best Practices for Safe Use of Generative Al Promoting a Culture of Security Awareness in AlLearning Outcomes:By the end of this course, participants will:Understand the fundamental concepts and types of Generative AI models.Recognize the diverse applications of Generative AI across various industries.Identify security risks and ethical concerns associated with Generative AI.Implement best practices for mitigating security threats and promoting ethical AI use.Develop a comprehensive security framework tailored to their organizational needs.Foster a culture of security awareness and responsibility in AI development and deployment.
Overview
Section 1: Understanding Generative AI
Lecture 1 What is Generative AI?
Lecture 2 Types of Generative AI models (e.g., GPT, DALL-E, BERT)
Lecture 3 Applications of Generative AI in various industries
Section 2: Threats and Risks Associated with Generative Al & 120 Examples
Lecture 4 Security Risks Associated with Generative Al
Lecture 5 Ethical Concerns and Bias in Al-Generated Content
Lecture 6 Data Poisoning and Prompt Injection Attacks
Lecture 7 10 Example of Data Poisoning in different Industries
Lecture 8 10 Example of Prompt Injection Attacks in different Industries Part 1
Lecture 9 10 Example of Prompt Injection Attacks in different Industries Part 2
Lecture 10 Model Inversion and Data Leakage
Lecture 11 10 Example of Model Inversion in different Industries Part 1
Lecture 12 10 Example of Model Inversion in different Industries Part 2
Lecture 13 10 Example of Data Leakage in different Industries Part 1
Lecture 14 10 Example of Data Leakage in different Industries Part 2
Lecture 15 Model Stealing and Its Implications
Lecture 16 20 Example of Model Stealing in different Industries
Lecture 17 Hallucinations in Generative Al
Lecture 18 Inadequate Sandboxing and Malicious Code Execution
Lecture 19 20 Example of Inadequate Sandboxing in different Industries
Lecture 20 20 Example of Malicious Code in different Industries
Section 3: Top Threats Presented by Public Al & Case Studies
Lecture 21 Top Threats Presented by Public Al
Lecture 22 Case Study 1
Lecture 23 Case Study 2
Lecture 24 Case Study 3
Lecture 25 Case Study 4
Section 4: Developing a Security Framework for Generative AI
Lecture 26 Creating Security Framework with example of Finance Institution
Lecture 27 Implementing GPT-4 for Customer Support in a Tech Company
Lecture 28 Implementing AI for Predictive Maintenance in Manufacturing
Lecture 29 Implementing AI for Autonomous Driving in the Automotive and Transportation
Section 5: Best Practices for Safe Use of Generative Al
Lecture 30 Best Practices for Safe Use of Generative Al
Section 6: Promoting a Culture of Security Awareness in Al
Lecture 31 Promoting a Culture of Security Awareness in Al
IT Security Professionals,IT Professionals,Cybersecurity Analysts, IT Security Managers, Security Architects,Data Scientists, Machine Learning Engineers, AI Researchers,Chief Information Officers (CIOs), Chief Technology Officers (CTOs), IT Project Managers,Compliance Officers, Legal Advisors, Risk Managers,Continuous Learners, Career Changers
![Generative Ai Security For Safe Use In Organizations]()
What you'll learn
Generative Al
Ethical Concerns and Bias in Al-Generated Content
Security Risks Associated with Generative Al
Data Poisoning and Prompt Injection Attacks
Model Inversion and Data Leakage
Model Stealing and Its Implications
Inadequate Sandboxing and Malicious Code Execution
Top Threats Presented by Public Al
Developing a Security Framework for Generative AI
120 Examples
Case Studies
Best Practices for Safe Use of Generative Al
Promoting a Culture of Security Awareness in Al
Requirements
Eager to learn Generative AI Security.
Description
This course provides a comprehensive understanding of Generative AI (GenAI) technologies, their applications across various industries, and the associated security and ethical considerations. Participants will gain insights into the types of Generative AI models, their potential vulnerabilities, and practical strategies for mitigating risks. Through detailed lectures, real-world examples, and case studies, this course aims to equip professionals with the knowledge and tools necessary to promote secure and ethical use of Generative AI.Security is a major concern in the deployment of Generative AI, and this course will delve into specific threats such as data poisoning, prompt injection attacks, model inversion, data leakage, and model stealing. Through detailed examples from different industries, participants will learn to identify and mitigate these risks. Ethical concerns, including bias in AI-generated content, will also be addressed, providing guidelines for ethical AI use.Participants will explore real-world case studies to understand the practical implications of these risks and learn to develop a comprehensive security framework tailored to their organizational needs. The course will emphasize best practices for the safe use of Generative AI, including the importance of sandboxing to prevent malicious code execution and promoting a culture of security awareness within organizations.By the end of the course, participants will be equipped with the knowledge and tools necessary to ensure the secure and ethical deployment of Generative AI technologies. Key Areas Covered Are:Applications of Generative Al in various industries-1Security Risks Associated with Generative AlEthical Concerns and Bias in Al-Generated ContentData Poisoning and Prompt Injection AttacksExample of Data Poisoning in different IndustriesExample of Prompt Injection Attacks in diferent IndustriesModel Inversion and Data LeakageExample of Model Inversion in different IndustriesExample of Data Leakage in different IndustriesModel Stealing and Its ImplicationsExample of Model Stealing in different IndustriesHallucinations in Generative AlInadequate Sandboxing and Malicious Code ExecutionExample of Inadequate Sandboxing in different IndustriesExample of Malicious Code in different IndustriesTop Threats Presented by Public Alcase studiescreating six steps security framework Best Practices for Safe Use of Generative Al Promoting a Culture of Security Awareness in AlLearning Outcomes:By the end of this course, participants will:Understand the fundamental concepts and types of Generative AI models.Recognize the diverse applications of Generative AI across various industries.Identify security risks and ethical concerns associated with Generative AI.Implement best practices for mitigating security threats and promoting ethical AI use.Develop a comprehensive security framework tailored to their organizational needs.Foster a culture of security awareness and responsibility in AI development and deployment.
Overview
Section 1: Understanding Generative AI
Lecture 1 What is Generative AI?
Lecture 2 Types of Generative AI models (e.g., GPT, DALL-E, BERT)
Lecture 3 Applications of Generative AI in various industries
Section 2: Threats and Risks Associated with Generative Al & 120 Examples
Lecture 4 Security Risks Associated with Generative Al
Lecture 5 Ethical Concerns and Bias in Al-Generated Content
Lecture 6 Data Poisoning and Prompt Injection Attacks
Lecture 7 10 Example of Data Poisoning in different Industries
Lecture 8 10 Example of Prompt Injection Attacks in different Industries Part 1
Lecture 9 10 Example of Prompt Injection Attacks in different Industries Part 2
Lecture 10 Model Inversion and Data Leakage
Lecture 11 10 Example of Model Inversion in different Industries Part 1
Lecture 12 10 Example of Model Inversion in different Industries Part 2
Lecture 13 10 Example of Data Leakage in different Industries Part 1
Lecture 14 10 Example of Data Leakage in different Industries Part 2
Lecture 15 Model Stealing and Its Implications
Lecture 16 20 Example of Model Stealing in different Industries
Lecture 17 Hallucinations in Generative Al
Lecture 18 Inadequate Sandboxing and Malicious Code Execution
Lecture 19 20 Example of Inadequate Sandboxing in different Industries
Lecture 20 20 Example of Malicious Code in different Industries
Section 3: Top Threats Presented by Public Al & Case Studies
Lecture 21 Top Threats Presented by Public Al
Lecture 22 Case Study 1
Lecture 23 Case Study 2
Lecture 24 Case Study 3
Lecture 25 Case Study 4
Section 4: Developing a Security Framework for Generative AI
Lecture 26 Creating Security Framework with example of Finance Institution
Lecture 27 Implementing GPT-4 for Customer Support in a Tech Company
Lecture 28 Implementing AI for Predictive Maintenance in Manufacturing
Lecture 29 Implementing AI for Autonomous Driving in the Automotive and Transportation
Section 5: Best Practices for Safe Use of Generative Al
Lecture 30 Best Practices for Safe Use of Generative Al
Section 6: Promoting a Culture of Security Awareness in Al
Lecture 31 Promoting a Culture of Security Awareness in Al
IT Security Professionals,IT Professionals,Cybersecurity Analysts, IT Security Managers, Security Architects,Data Scientists, Machine Learning Engineers, AI Researchers,Chief Information Officers (CIOs), Chief Technology Officers (CTOs), IT Project Managers,Compliance Officers, Legal Advisors, Risk Managers,Continuous Learners, Career Changers
