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AI Guardrails & Cybersecurity - Generative AI, MCP Hands On

Course Content

17 sections • 87 lectures • 6h 35m total length

4.9 (2 reviews)

FREE

6.5 hours on-demand video
Access on mobile and TV
Full lifetime access
Certificate of completion
  • Welcome to AI Guardrails
  • Course Contents
  • 10,000 Foot view on Language Models
  • LLM Inference Parameters
  • Vector Embedding
  • RAG – Retrieval Augment Generation
  • Introduction to Large Language Models (LLMs)
  • LLM Constraints in Modern AI Applications
  • LLM Constraints – Hallucination
  • LLM Constraints – Bias and Ethical Concerns
  • LLM Constraints – Data Privacy and Security
  • LLM Constraints – Output Alignment
  • Understanding Constraints in Large Language Models
  • Introduction to AI Guardrails
  • Applications of AI Guardrails
  • Understand AI Guardrails
  • Prompts
  • Prompt Injection
  • Prompt Guard
  • Hands on with prompt-guard model
  • Llama Guard Theory
  • Llama Guard Prompt
  • Hands on with Llama Guard Model
  • Llama Guard 3 – Vision Theory
  • Hands On with Llama Guard 3 – Vision
  • Hallucination
  • Detect Hallucination
  • Detect Hallucination with phi3-hallucination-judge model
  • Detect Hallucination with hallucination-evaluation-model
  • AWS Bedrock – Guardrails Components
  • Introduction to Bedrock
  • Bedrock-Guardrail – Hands On
  • Multimodal Guardrails- Image
  • Introduction to Garak
  • Garak Probes
  • Install Garak
  • Detect LLM Vulnerability – Encoding
  • XFilteration
  • Detect LLM Vulnerability – XFilteration
  • Detect LLM Vulnerability – Profanity
  • Inroduction to AI Agents
  • Agentic Design with Runtime
  • Introduction to CrewAI
  • Introduction to Penetration Testing
  • Hands On Penetration Test using ZapProxy Too
  • AI Agent for Cybersecurity/Penetration Testing
  • Penetration Test using Tools Call
  • Run Cybersecurity Penetration Test with AI Agent
  • Run and Evaluate Cybersecurity Penetration Test with AI Agent
  • Agentic Use Case with Multimodal, Multi-Hop and ReAct Architecture
  • ReACT Prompt for AI Agents
  • Run the Agent
  • Multi Agent with Multi Tools
  • What is Haystack Framework
  • Types of Evaluators
  • Evaluators Runtime
  • Amazon Bedrock Evaluator – Retriever and Generate(RAG)
  • Amazon Bedrock Evaluator – Model as A Judge
  • Faithfulness (aka Hallucination) Evaluator
  • RAG Evaluator
  • SAS Evaluator, ContextRelevance Evaluator
  • What is GuardrailsAI
  • Working of GuardrailsAI
  • GuardrailsAI – RAIL Specifications
  • GuardrailsAI – Understanding the Output Component
  • GuardrailsAI – Understanding the Validators
  • GuardrailsAI – Built-in Validators
  • GuardrailsAI – Validator OnFail Policies
  • GuardrailsAI – Understanding the Prompt Component
  • GuardrailsAI – Understanding the Guard Component
  • Example 1 – Extracting Patient Data from Physician Notes
  • Example 2 – Detecting Competitor Presence
  • Example 3 – Validator Chains – Competitor Analysis & Toxic Language Detection
  • Example 4 – Create a Custom Validator
  • Understanding GuardrailsAI Framework
  • 25 Limitations of GuardrailsAI
  • Colang
  • Example – Execution
  • Understanding Nemo Runtime
  • Runtime Nemo Multiple LLM Info Log
  • Runtime Nemo Single LLM Config
  • Runtime Nemo Single LLM Info Log
  • Evolution of MCP – Current Solutions and their Limitations
  • Client Server Architecture
  • MCP Architecture
  • MCP Server Components
  • MCP Transport Types
  • MCP Communication
  • MCP Flow Diagram – Server, Client and Host communication over Transport Layer
  • MCP E2E Flow

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