Automating Cybersecurity Operations with AI

Course details

Stop drowning in alerts—let AI handle the busywork while you make the calls that matter. In this fast, hands-on workshop, you’ll learn how artificial intelligence can automate high‑volume security tasks—threat detection, alert triage, phishing analysis, and incident response—without writing code or building custom models. You’ll also practice defending against adversarial AI. Using real datasets and free tools (ChatGPT, Google Colab with run‑ready notebooks, and Splunk Free), you’ll assemble no‑code workflows that turn scattered signals into clear decisions, dashboards, and documented actions.

Learning objectives
Explain the foundational concepts of artificial intelligence and its applications within cybersecurity.
Apply AI techniques to detect threats, analyse security incidents, and coordinate rapid response actions.
Implement AI-driven automation to streamline Security Operations Center (SOC) workflows and enhance efficiency.
Develop strategies to counter adversarial AI techniques and defend against malicious automation threats.

Concepts

Welcome to the course

Intro video to course
Module introduction
The modern SOC crisis - Alert fatigue and resource gaps
AI as the force multiplier - Transforming security operations
Hands-On - Manual vs. AI-automated alert analysis

AI Models in Cybersecurity - What You Need to Know

Demystifying AI - ML vs. DL vs. LLMs for security practitioners
AI model types in security operations - Detection, classification, and NLP
Hands-on - Building your first anomaly detector with Python and AI

Building Your AI-Powered Security Toolkit

Setting up your security automation Environment
Connecting to AI APIs - ChatGPT and Claude integration
Hands-on - Your first AI security pipeline

Automated Phishing Detection with AI

Module introduction
Anatomy of phishing attacks - What makes them work
Building blocks of AI phishing detection systems
Hands-on - Building a complete phishing detector with Python and AI

Network Anomaly Detection with Machine Learning

Understanding network logs and security anomalies
Machine learning for anomaly detection - Algorithms and approaches
Hands-on - Building a network anomaly detection system

Malware Analysis and IOC Extraction Automation

Indicators of compromise - The language of threat intelligence
Malware classification and threat intelligence APIs
Hands-on - Automated IOC extraction and threat intelligence generation

AI-Assisted Alert Triage and Prioritization

Module introduction
The alert triage challenge - From noise to signal
Building intelligent triage systems - Enrichment and scoring
Hands-on - Building an AI-powered alert triage system

Automated Investigation and Evidence Collection

The art of security investigation - From alert to attack story
Log correlation and timeline reconstruction techniques
Hands-on - Building an automated investigation engine

Orchestrating Automated Response Actions

Response automation and SOAR - Speed vs. safety
API-driven response - Integrating security tools
Hands-on - Building an automated incident response playbook

Building AI-Powered Security Playbooks with RAG

Module introduction
From static SOPs to dynamic AI playbooks
Understanding RAG - How AI accesses your security knowledge
Hands-on - Building a security analyst AI assistant with RAG

End-to-End autonomous security pipelines

Event-driven security architecture - From reactive to proactive
Workflow orchestration and human-in-the-loop decision making
Hands-on - Building a production-ready phishing response pipeline

Defending Against AI Threats and Future-Proofing

The adversarial AI threat landscape - How attackers weaponize AI
Defensive AI - Hardening models and detecting AI-generated threats
Hands-on - Adversarial testing and the future of AI security
Course wrap-up video