Hello everyone and welcome.
In this video I will walk you through how the course is organized.
We will begin with LM fundamentals where we will learn about LM model architecture, its capabilities,
and also expose the inherent vulnerabilities that it has.
Then we would move on to AI guardrails.
Why is there a need for AI guardrails?
We would expose some of the security risks and business justification for implementing protective measures.
Then on we would cover some of the core techniques like vector embeddings and retrieval augmented generation
concepts.
Then we would introduce you to the user input guardrails where we would use a couple of models like
Prompt Guard and Llama guard from meta, which are open source models to detect input detection and
content moderation.
Then we would introduce you to the foundation model guardrails.
We would use Pi three hallucination judge and evaluation models for detecting output quality.
Then we would move on to understanding Garrick, which is an open source vulnerability scanner that
provides standardized detection for comprehensive LLM security testing.
Then we would dive deeper into cybersecurity using AI agents.
We would create autonomous software programs that would help us detect.
The vulnerabilities and help the.
Help the system with better decision making.
And then we would have evaluation frameworks where we would use different metrics driven assessments
for hallucination detection and context relevancy, followed by AWS bedrock integration, where we would
use the bedrock implementation for AI guardrails.
And then we would use couple of open source tools like guardrails, AI, and guardrails framework for
implementing custom security implementations.
Thank you everyone and hope you enjoyed the course.