Securing LLMs: Build Secure LLM Products Using Python and Streamlit

In this course you'll gain a deep understanding of the capabilities and security challenges of Large Language Models (LLMs). LLMs are at the forefront of technological innovation, but along with their tremendous potential they bring new risks that must be understood and addressed with practical solutions.
By the end of this course, you'll possess the knowledge and skills required to work confidently with LLMs, tackle their challenges, address hallucinations, and secure these powerful tools by building security layers. Whether you're looking to enhance your AI expertise or stay at the forefront of technological advancements, this course is your gateway to mastering LLMs and unlocking their full potential by keeping them secure and reliable.
What you’ll learn and how to apply it
By the end of this course, the learner will be able to understand the different security threats to LLMs and build products that fortify LLMs against these threats.
This course is for you because
You are a programmer interested in building customer-facing products using Large Language Models (LLMs).
You are a security professional who wants to stay updated with kinds of security threats in cutting-edge AI development like LLMs.
You are looking to move into a Data Science role where LLM products are used/built.
You want to become more proficient at building applications with LLMs.
Generative AI (LLM) is an adjacent technology that you would like to understand better.
Prerequisites
Python (Intermediate)
Streamlit (Intermediate)
Access to an LLM
Course Materials
https://github.com/bhav09/Security-for-LLMs