AI for Cyber Security : Threat Detection, SOC Automation - Master the Basics of Artificial Intelligence in Cybersecurity – No Prior AI Knowledge Needed
Description
Artificial Intelligence is redefining the future of cybersecurity — and this course is your complete roadmap to mastering it.
In AI for Cybersecurity: Threat Detection & SOC Automation, you’ll learn how AI, Machine Learning (ML), and Deep Learning (DL) are transforming how organisations detect, prevent, and respond to cyber threats.
This program blends real-world labs, tools, and automation workflows to prepare you for the next generation of AI-driven cybersecurity roles — from SOC analyst to security automation engineer.
What You’ll Learn Across Modules:
Module 1: Introduction to AI in Cybersecurity
Learn the foundations of AI, ML, and DL, explore their evolution, benefits, and challenges, and see how AI integrates into real-world SOC environments with tools like Darktrace and CrowdStrike.
Module 2: AI for Threat Detection
Understand machine learning for anomaly detection, supervised vs unsupervised learning, and how AI enhances IDS systems like Suricata for faster and smarter threat identification.
Module 3: AI for Threat Intelligence
Discover how Natural Language Processing (NLP) is used to analyse phishing data, automate enrichment with APIs such as VirusTotal and AbuseIPDB, and strengthen threat intel pipelines.
Module 4: AI for SOC Automation
Explore AI-powered SOAR platforms, playbook automation, and the balance between human and AI decision-making in modern security operations.
Module 5: AI for Incident Response
Learn how AI assists in decision-making, predicts breach impact, and optimises real-time alert management and forensic reconstruction.
Module 6: AI for User Behaviour Analytics (UBA)
Apply ML models to baseline user activity, detect insider threats, and use graph-based analytics for behavioural risk scoring.
Module 7: AI for Malware Analysis
Perform AI-driven malware classification using sandbox analysis, embeddings, and the EMBER dataset to detect and forecast malicious behaviour.
Module 8: AI in Cloud Security
Secure cloud environments using AI for misconfiguration detection, anomaly analysis, and posture management with AWS GuardDuty or Azure Defender.
Module 9: AI in Network Security
Analyse network traffic, identify DDoS patterns, and apply ML models for encrypted traffic analysis and zero-trust segmentation.
Module 10: AI in Endpoint Security
Automate EDR workflows, apply federated learning, and detect ransomware with behaviour-based AI models.
Module 11: Limitations & Ethical Considerations
Study bias, false positives, and privacy issues in AI systems to ensure ethical cybersecurity practices.
Module 12: Future of AI in Cybersecurity + Capstone Project
Design an AI-augmented SOC workflow, integrating tools, automation, and analytics for intelligent cyber defence.
By the end of this course, you’ll be able to build, automate, and manage AI-powered defence systems, preparing you for cutting-edge roles in cybersecurity and AI operations.
Who this course is for:
- This course is designed for cybersecurity professionals who want to integrate AI into real-world defense, threat detection, and incident response workflows.
- It is ideal for SOC analysts, blue teamers, and incident responders looking to upskill in AI-based security automation and intelligent threat detection.
- t is also perfect for AI and machine learning enthusiasts who wish to understand their application in cybersecurity through hands-on labs and projects.
- Students, IT professionals, and security engineers who aspire to transition into next-generation AI-driven SOC or automated defense roles will greatly benefit from this course.
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