Agentic AI for Cybersecurity by Pearson
3h 30mIntermediate2026-08-17
Authors
Pearson
Course details
Agentic AI is reshaping cybersecurity, enabling systems that detect threats, respond to incidents, and analyze vulnerabilities with minimal human intervention. In this course, learn how to build, deploy, and manage autonomous security systems powered by AI agents. Start with the foundational concepts of AI agents, retrieval-augmented generation (RAG), and the interoperability protocols that connect agents into a seamless network. Explore tools such as LangChain, LlamaIndex, and CrewAI to strengthen your cybersecurity toolkit, and automate threat detection, incident response, and vulnerability analysis with state-of-the-art AI frameworks. Examine the security implications of inter-agent communication, and build secure AI applications with hands-on guidance. Whether you're a SOC analyst, cybersecurity architect, or AI developer, this course prepares you to develop and orchestrate agentic AI systems built for the demands of modern cyber defense.
Learning objectives
Explain the foundational concepts of AI agents, RAG, and agent interoperability protocols.
Build and deploy autonomous security systems using frameworks such as LangChain, LlamaIndex, and CrewAI.
Automate threat detection, incident response, and vulnerability analysis with AI agents.
Assess the security implications of inter-agent communication.
Orchestrate agentic AI systems tailored for cybersecurity.
Learning objectives
Explain the foundational concepts of AI agents, RAG, and agent interoperability protocols.
Build and deploy autonomous security systems using frameworks such as LangChain, LlamaIndex, and CrewAI.
Automate threat detection, incident response, and vulnerability analysis with AI agents.
Assess the security implications of inter-agent communication.
Orchestrate agentic AI systems tailored for cybersecurity.
Concepts
Introduction
- Agentic AI for Cybersecurity - Introduction
Foundations of Agentic AI Systems
- Learning objectives
- What is an AI agent
- Understanding the different types of AI agents
- The agentic loop - ReAct, Plan-and-Execute
- Exploring general use cases of agentic AI for cybersecurity
- Resources and GitHub repositories for this course
Retrieval-Augmented Generation (RAG), Agentic RAG, and Model Context Protocol (MCP)
- Learning objectives
- Introducing RAG
- Understanding agentic RAG
- Vector databases and embedding models
- Introducing MCP
- Using open weight models and introducing Hugging Face
- Introducing Ollama-AnythingLLM-vLLM-ComfyUI
- Introducing WebMCP
Enabling Interoperability - Protocols for the Internet of Agents
- Learning objectives
- Introducing the Agent2Agent protocol
- Comparing A2A with AGNTCY and ACP
- Agent Name Service (ANS) for secure Al agent discovery
- Security implications of inter-agent communication
Orchestration with LangChain, LangGraph, and LlamaIndex
- Learning objectives
- Introducing LangChain and LangGraph
- Building your first AI application with LangChain
- Evaluating AI applications with LangSmith
- Introducing LlamaIndex
- Building an agent with LangGraph
- Building an MCP server with FastMCP
Exploring CrewAI, n8n, Apache Airflow, and Other Agentic Frameworks
- Learning objectives
- Introducing CrewAI for agent collaboration
- Exploring n8n for low-code agentic workflows
- Introducing Apache Airflow for data-intensive agent tasks
The Architect's Cockpit - AI-Powered IDEs and Coding Agents
- Learning objectives
- Introducing Cursor
- Using Windsurf and Cascade
- Understanding agent skills, rules, memories, and workflow files
- Exploring OpenAI's Codex
- Using Claude Code
- Using AI for secure code review
- Using additional tools like Antigravity, OpenCode, and Warp
Conclusion
- Agentic AI for Cybersecurity - Summary