Overview What You Will Learn Curriculum Instructor

Course Overview

The cybersecurity landscape is undergoing a major transformation with the rise of artificial intelligence. Traditional automation and playbook-driven SOC workflows are no longer sufficient to counter modern adversaries who increasingly use AI-enabled tactics. Security teams face growing challenges such as alert fatigue, resource limitations, and the need for faster detection and response. Organizations now require skilled professionals who can design and manage next-generation solutions powered by autonomous AI agents to strengthen both defense and offense in cybersecurity operations.

This comprehensive, hands-on course equips you with the knowledge and skills to build, deploy, and secure AI-driven agents using industry-leading frameworks such as LangChain and LangGraph. Participants will progress from foundational concepts of agentic AI and defensive SOC assistants to building offensive reconnaissance agents and orchestrating multi-agent systems. With a strong focus on practical implementation, the course provides structured labs, including a capstone Purple Team simulation where you design and execute live-fire exercises between Red Team and Blue Team agents in a controlled environment. Additionally, the course emphasizes critical aspects of governance, ethics, and secure deployment to ensure responsible adoption of agentic AI systems.

By the end of this course, you will be able to design and implement AI-powered cybersecurity agents that address real-world challenges across Security Operations, Threat Intelligence, and Red/Blue Teaming.

What You Will Learn

  • Build and deploy a defensive SOC Assistant agent capable of parsing SIEM logs, enriching alerts with threat intelligence, and automatically generating structured incident reports.
  • Create and operate an offensive reconnaissance agent that automates network scanning and information gathering in a controlled, ethical lab environment.
  • Architect a complete multi-agent system using LangGraph to simulate a Purple Team exercise, where your defensive agent detects and responds to actions from your offensive agent in real-time.
  • Implement essential security and governance layers into agentic systems, including human-in-the-loop approval steps, to ensure safe, reliable, and responsible operation in a production environment.

Program Curriculum

  • From Automation to Agency
  • Introduction to LangChain Framework
  • Lab 1: Your Development Sandbox
  • Chapter 1 Quiz

  • Use Case: Taming Alert Fatigue
  • Structuring Data with LLMs and Output Parsers
  • Lab 2: AI-powered SIEM Log Analysis
  • Chapter 2 Quiz

  • Overcoming Static Knowledge with RAG
  • Vector Databases for Semantic Search
  • Lab 3: Building a Threat Intelligence Pipeline
  • Chapter 3 Quiz

  • The "Red Team" Agent: Principles and Ethics
  • Creating Custom Tools for Security Agents
  • Lab 4: The Automated Recon Agent
  • Chapter 4 Quiz

  • The Need for Agent Teams
  • Introduction to LangGraph
  • Multi-agent Lab
  • Chapter 5 Quiz

  • Designing the Simulation
  • Lab 5: Building and Running the Simulation
  • Chapter 6 Quiz

  • The New Attack Surface of Agentic AI
  • Principles of Secure Agent Design
  • Lab 6: Implementing Agent Guardrails
  • Chapter 7 Quiz
Load more modules

Instructor

Manas Dasgupta

Manas holds an MSc in AI-ML from Liverpool John Moores University (LJMU), UK. His expertise includes Generative AI (RAG application development using frameworks like LangChain and LlamaIndex), Machine Learning, Data Science, and Predictive Analytics, with a focus on techniques such as Supervised and Unsupervised Learning, Deep Neural Networks, and Clustering.

His research focuses on Natural Language Processing (NLP) using Deep Learning methods like Siamese Networks, Encoder-Decoder techniques, and Language Embeddings (e.g., BERT).

With over 20 years of experience in IT, primarily in Financial Services, Manas has developed solutions for top banks and institutions, including Natwest, ANZ, KBC, Visa, and Experian.

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