Automating Cybersecurity Operations with AI

Overzicht

Transform security operations with practical AI-driven automation. In this hands-on course, cybersecurity professionals learn how to use Python, machine learning, and large language models (LLMs) such as ChatGPT and Claude to automate threat detection, alert triage, incident response, and security operations center (SOC) workflows.

Through real-world coding exercises and security datasets, you will build automated phishing detection systems, network anomaly detection models, threat intelligence enrichment pipelines, and AI-assisted investigation tools. You'll also learn how to integrate security APIs, develop automated response playbooks, implement Retrieval-Augmented Generation (RAG) for security knowledge management, and design production-ready security automation architectures.

By the end of the course, you will have created a complete AI-powered SOC platform that combines detection, investigation, response, and analyst assistance into a unified workflow. Whether you're a SOC analyst, security engineer, or cybersecurity professional, you'll gain practical skills in AI cybersecurity automation, Python security scripting, SOAR workflows, incident response automation, and AI-powered threat analysis that can be applied immediately in modern security environments.

Lesprogramma

• Foundations of AI-Driven Security Operations
This foundational module establishes the context, architecture, and practical setup required for AI-powered security automation. Students learn why automation is critical for modern SOCs, understand different AI model types and their security applications, and set up their Python development environment with security-focused libraries and AI API integrations. Through hands-on demonstrations, learners compare manual versus automated workflows and build their first AI-integrated security scripts.
• AI-Powered Threat Detection and Analysis
This module focuses on building automated detection systems for common security threats using Python and AI. Students develop practical skills in phishing detection, network anomaly identification, malware classification, and threat intelligence automation. Each lesson combines machine learning techniques with large language model capabilities to create sophisticated yet accessible detection systems that can be deployed in production environments.
• Automating Incident Response Workflows
This module teaches students to build end-to-end automated incident response systems that handle the complete lifecycle from alert triage through containment and documentation. Students create sophisticated workflows that enrich alerts with threat intelligence, perform automated investigations across multiple data sources, execute containment actions via APIs, and integrate with ticketing systems. The focus is on reducing mean time to respond (MTTR) while maintaining high-quality incident handling.
• Production-Ready AI Security Automation
This advanced module prepares students to deploy AI security automation in production environments. Students learn to build intelligent security assistants using retrieval-augmented generation (RAG), design event-driven automation architectures, implement continuous monitoring systems, and defend against adversarial AI attacks. The module culminates in understanding compliance considerations, model evaluation, and the future trajectory of AI in security operations.

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Gegeven door

Aseem Singhal and Starweaver

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Vakgebieden

Information Security (InfoSec)