Generative AI Risks & Cybersecurity: LLM Security

Data Poisoning, Model Bias, Prompt Injection, AI Ethics & Governance | Protect AI Systems from Cyber Threats

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.