Generative AI Security for Safe Use in Organizations

Name: Generative AI Security for Safe Use in Organizations
Rating: 4.3 (46 reviews)

Best Practices, Case Studies, Security Frameworks and 120 Examples

Created byEdcorner Learning

Last updated 5/2026

English

English [Auto],

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

Course content

6 sections • 31 lectures • 2h 9m total length

What is Generative AI?5:35
Generative AI uses GANs, VAEs, and autoregressive models like GPT-4 to create text, images, music, and more, while raising concerns about bias, deepfakes, data security, and ethical guidelines.
Types of Generative AI models (e.g., GPT, DALL-E, BERT)2:42
Explore how generative ai models like GPT, DALL-E, BERT, GANs, and VAEs use unique architectures to create original content, including text and image generation and context-aware embeddings.
Applications of Generative AI in various industries6:11
Generative AI shapes content creation, personalization, and customer engagement across industries such as marketing and advertising, entertainment, healthcare, finance, retail, education, manufacturing, gaming, art, and automotive.

Security Risks Associated with Generative Al6:02
Explore security risks of generative ai, including unauthorized access, data leaks, and GDPR non-compliance. Assess operational and ethical challenges, and learn mitigations like encryption, consent, and human oversight.
Ethical Concerns and Bias in Al-Generated Content7:13
Examine how training data biases shape AI-generated content and amplify stereotypes and misinformation. Explore mitigation strategies, governance, transparency, and responsible use to ensure fair and accountable AI in organizations.
Data Poisoning and Prompt Injection Attacks6:08
Analyze data poisoning and prompt injection attacks that threaten generative ai, causing model degradation and data exposure, and explore mitigation strategies like data validation, audits, adversarial training, and input sanitization.
10 Example of Data Poisoning in different Industries4:32
10 Example of Prompt Injection Attacks in different Industries Part 11:41
10 Example of Prompt Injection Attacks in different Industries Part 21:44
Model Inversion and Data Leakage5:46
Explore model inversion and data leakage in generative AI, their privacy and security impacts, and practical mitigations like differential privacy, regularization, access control, and monitoring and logging.
10 Example of Model Inversion in different Industries Part 12:15
10 Example of Model Inversion in different Industries Part 22:09
10 Example of Data Leakage in different Industries Part 11:57
10 Example of Data Leakage in different Industries Part 21:51
Model Stealing and Its Implications4:21
20 Example of Model Stealing in different Industries3:58
Hallucinations in Generative Al6:41
Examine how hallucinations arise in generative ai for nlp and image generation, from data quality and distribution to model complexity, then explore practical mitigation strategies.
Inadequate Sandboxing and Malicious Code Execution5:46
Understand how inadequate sandboxing and malicious code execution threaten generative ai in organizations, risking data breaches, system compromise, and the need for robust isolation and strict controls.
20 Example of Inadequate Sandboxing in different Industries3:11
20 Example of Malicious Code in different Industries3:07

Top Threats Presented by Public Al8:15
Identify top threats of public AI, including data privacy exposure, data harvesting, deepfakes, bias, accountability gaps, dependency risk, and social disruption, plus regulatory and technical mitigations for safe organizational use.
Case Study 12:09
Explore how generative AI misuse creates deepfakes and misinformation, eroding trust and enabling political manipulation, with detection tools and legislation as mitigation.
Case Study 21:40
Explore how AI generated fake news can spread rapidly, with case study two showing GPT-3 producing convincing but fabricated articles, and learn responses like fact checking and public awareness campaigns.
Case Study 31:31
Explore how generative ai automates phishing emails, enabling personalized, harder-to-detect attacks that drive data breaches and financial losses, and learn ai powered defenses and employee training to mitigate them.
Case Study 41:58
Demonstrate how generative AI can perpetuate bias in hiring, lending, and law enforcement from biased training data. Promote mitigation through bias audits, diverse data, and human oversight.

Creating Security Framework with example of Finance Institution8:40
Develop a comprehensive security framework for generative AI in organizations, covering risk assessment, policy development, technical safeguards, governance, training, and continuous improvement, with a finance industry example.
Implementing GPT-4 for Customer Support in a Tech Company2:37
Implementing AI for Predictive Maintenance in Manufacturing3:14
Implementing AI for Autonomous Driving in the Automotive and Transportation3:06

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
Security Risks Associated with Generative Al
Ethical Concerns and Bias in Al-Generated Content
Data Poisoning and Prompt Injection Attacks
Example of Data Poisoning in different Industries
Example of Prompt Injection Attacks in diferent Industries
Model Inversion and Data Leakage
Example of Model Inversion in different Industries
Example of Data Leakage in different Industries
Model Stealing and Its Implications
Example of Model Stealing in different Industries
Hallucinations in Generative Al
Inadequate Sandboxing and Malicious Code Execution
Example of Inadequate Sandboxing in different Industries
Example of Malicious Code in different Industries
Top Threats Presented by Public Al
case studies
creating six steps security framework
Best Practices for Safe Use of Generative Al
Promoting a Culture of Security Awareness in Al

Learning 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.

Who this course is for:

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

Explore related topics

Course content

Understanding Generative AI3 lectures • 14min

Threats and Risks Associated with Generative Al & 120 Examples17 lectures • 1hr 8min

Top Threats Presented by Public Al & Case Studies5 lectures • 16min

Developing a Security Framework for Generative AI4 lectures • 18min

Best Practices for Safe Use of Generative Al1 lecture • 7min

Promoting a Culture of Security Awareness in Al1 lecture • 7min

Requirements

Description

Who this course is for: