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The AI for Cyber Security Training Syllabus is a practical, job-focused program that teaches how to apply AI and Machine Learning in real-world cyber-defense scenarios. It covers threat detection, security analytics, and automated incident response. This course is ideal for SOC analysts, security engineers, ethical hackers, and IT professionals aiming to work with AI-driven cybersecurity solutions.
Mode: Hands-on Labs + Live Attack & Defense Scenarios
Target Roles:
SOC Analyst (L1 / L2 / L3)
Cyber Security Engineer
Threat Hunter
Blue Team / Purple Team Engineer
AI-Driven Security Analyst
Duration of Training : 40–50 Hours
Batch type : Weekdays/Weekends
Mode of Training : Classroom/Online/Corporate Training
Detailed Syllabus • Hands-on Labs • Assignments • Support-Focused • Implementation
Curriculum Designed by Experts
Topics
Why AI is critical in cyber security
AI vs traditional security tools
ML vs DL vs GenAI in security
Attack lifecycle & MITRE ATT&CK
Data types in cyber security
Assignment
Map AI use cases to cyber attack stages
Topics
Python for security analytics
Log parsing & normalization
Feature engineering for security data
Handling noisy & imbalanced datasets
Hands-On
Parse firewall & SIEM logs using Python
Topics
Supervised vs unsupervised learning
Anomaly detection techniques
Classification of malicious activity
Model evaluation for security use cases
Labs
Detect malicious login attempts
Build brute-force detection model
Topics
Static vs dynamic malware analysis
Feature extraction from binaries
ML-based malware classifiers
Evasion techniques & countermeasures
Project
Build AI-based malware detection system
Topics
AI-powered IDS/IPS
Flow-based trafic analysis
Deep learning for network threats
Encrypted trafic challenges
Hands-On
Build ML-based IDS using real datasets
Topics
AI-assisted alert triage
False positive reduction
UEBA (User Entity Behavior Analytics)
SOAR integration
Scenario
SOC flooded with alerts — automate prioritization.
Topics
NLP for email analysis
Phishing URL detection
Financial fraud detection models
Behavioral analytics
Project
Phishing detection using NLP & ML
Topics
LLMs for threat intelligence
AI-assisted incident response
Attack simulation using AI
Secure use of GenAI in enterprises
Hands-On
Build AI chatbot for SOC assistance
Topics
AI-driven threat intel enrichment
IOC correlation
Dark web monitoring concepts
Predictive threat modeling
Assignment
Build AI-based threat scoring system
Topics
AI in cloud security posture management
EDR/XDR analytics
Container & Kubernetes security (AI view)
Zero Trust with AI
Topics
Deploying ML models securely
Adversarial ML attacks
AI model poisoning
Ethics & compliance (GDPR, AI Act)
Scenario
Attackers targeting your ML models.
Topics
AI-assisted IR workflow
Root cause analysis
Threat hunting using ML
Reporting & compliance
1⃣ AI-Powered SOC Platform
✔ Log ingestion
✔ Threat detection models
✔ Alert prioritization
✔ Automated response
2⃣ AI-Based Intrusion Detection System
✔ Network trafic analysis
✔ Anomaly detection
✔ Live alerting
✔ Apply AI in real SOC environments
✔ Build ML models for security use cases
✔ Reduce false positives
✔ Automate incident response
✔ Prepare for AI-driven cyber roles
Python, Pandas, Scikit-learn
TensorFlow / PyTorch (intro)
ELK / SIEM concepts
Wireshark, Zeek
MITRE ATT&CK
GenAI / LLM APIs
AI Security Engineer
SOC Analyst (AI-Driven)
Threat Detection Engineer
Cyber Security Data Scientist
CompTIA Security+ (AI concepts)
CEH (AI use cases)
CISSP (future-ready domains)
AI Security specializations
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