Risks and Cybersecurity in Generative AI

Overview

Securing the Future:

Mitigating Risks in AI Innovation What you'll learn:

Understand the core concepts of generative AI and associated cybersecurity risks.Identify and analyze potential vulnerabilities within AI systems.Learn strategies to mitigate risks including data poisoning and model bias.Explore ethical considerations and best practices in AI development and usage. The course "Risks and Cybersecurity in Generative AI" offers a comprehensive exploration into the intersection of artificial intelligence and cybersecurity.

This course is designed to provide you with a thorough understanding of the potential risks and security measures necessary for deploying generative AI technologies safely and responsibly.Starting with an introduction to the basics of AI and generative models, you will learn about the broad applications and benefits of generative AI, followed by an initial look at AI security considerations. The course progresses into a detailed examination of core cybersecurity risks such as data privacy, breaches at AI service providers, and the evolution of threat actors, equipping you with strategies to protect sensitive information and mitigate risks.Further, you will delve into specific attack vectors and vulnerabilities unique to AI, including data leakage, prompt injections, and the challenges of inadequate sandboxing.

Each module is structured to provide practical knowledge through real-world examples and demonstrative sessions, enhancing your learning experience.The course also addresses network-level risks and AI-specific attacks, covering critical areas like Server Side Request Forgery (SSRF), DDoS attacks, data poisoning, and model bias. The final modules focus on legal and ethical considerations, guiding you through navigating intellectual property challenges and promoting ethical guidelines in AI development and usage.By the end of this course, you will be well-prepared to assess, address, and advocate for robust cybersecurity practices in the field of generative AI, ensuring these technologies are developed and deployed with the highest standards of security and ethical considerations.

Syllabus

• Introduction to Generative AI
Overview of AI and Machine Learning
Introduction to Generative Models
Applications and Benefits of Generative AI
• Fundamentals of AI and Cybersecurity
Definition and Scope of Cybersecurity in AI
Initial Exploration of AI Security Considerations
• Core Cybersecurity Risks in AI
Data Privacy and Security
Breaches at AI Service Providers
Evolution of Threat Actors
• Specific Vulnerabilities in AI Systems
Data Leakage Issues
Prompt Injections and Exploitation
Challenges of Inadequate Sandboxing
• Mitigation Strategies for AI Security
Techniques to Mitigate Data Poisoning
Addressing and Reducing Model Bias
Strategies for Protecting Sensitive Information
• Network-Level Risks and AI-Specific Attacks
Understanding Server Side Request Forgery (SSRF)
Defense Against DDoS Attacks
Identifying and Preventing Data Poisoning
• Ethical and Legal Considerations in AI Development
Navigating Intellectual Property Challenges
Ethical Guidelines in AI Development and Implementation
• Practical Knowledge and Real-World Applications
Real-World Case Studies of AI Security Incidents
Demonstrative Sessions on Security Practices
• Designing Robust Cybersecurity Practices in Generative AI
Building Secure AI Systems
Advocacy for Ethical and Secure AI Deployment
• Course Summary and Final Insights
Key Takeaways and Best Practices
Preparing for Future Developments in AI Security

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Taught by

Dr. Amar Massoud

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Subjects

Information Security (InfoSec)