This module explores the comparison between no-code and code-first AI frameworks, focusing on their application in security automation. Learners will understand the strengths and limitations of tools like n8n and LangChain, and develop the ability to choose the right framework for specific security tasks.

This module guides learners through the process of building and securing AI workflows using n8n, covering integration with AI services, threat intelligence APIs, and the implementation of RAG systems. It provides hands-on practice with Docker Compose and no-code agents to automate security tasks, while emphasizing the importance of security best practices in agentic workflows.

This module covers the development of an agentic AI system for attack surface management, focusing on integrating asset discovery, enumeration fingerprinting, and vulnerability detection. Learners will gain hands-on experience in building a no-code ASM solution using tools like n8n and open-source platforms. The module emphasizes practical implementation of modular workflows for real-world security applications.

This module covers practical techniques for testing internal network segmentation in PCI-DSS compliant environments. Learners will gain hands-on knowledge of tools and methods such as TCP SYN scans, UDP scans, and n8n workflows to validate security controls and prevent unauthorized access. It also introduces the integration of AI agents to automate and enhance segmentation testing processes.

This module teaches learners how to implement browser automation tools using MCP, analyze web application attack surfaces, and detect common vulnerabilities such as XSS and SQL injection. It covers practical techniques for testing web applications dynamically and understanding security risks in real-world scenarios.

This module delves into the practical aspects of creating and integrating exploitation tools, generating targeted payloads, and producing structured security reports. Learners will explore methodologies for designing modular security tools and understand how to translate testing outcomes into professional documentation.

This module focuses on building and enhancing CTI (Cyber Threat Intelligence) agents using tools like n8n, Qdrant, and Shodan. Learners will explore how to integrate AI, automate vulnerability research, and implement effective alerting systems for cybersecurity monitoring.

This module explores the design and implementation of scalable multi-agent systems in cybersecurity, with a focus on resilience, communication protocols, and ethical considerations. Learners will gain insights into building reliable and secure automation pipelines while addressing the risks of misuse and ensuring responsible disclosure practices.