AI in SOC (Security Operations Center) – Full Syllabus

Module 1: Introduction to SOC and AI

• What is a Security Operations Center (SOC)?
• Core functions of a SOC
• Challenges faced by traditional SOCs
• Role of AI and ML in modern SOCs
• Benefits of AI-driven SOCs

Module 2: Log Analysis & Data Collection

• Types of data collected in SOC (network, endpoints, firewalls, etc.)
• Log formats: Syslog, NetFlow, JSON
• Centralized logging systems (SIEM basics)
• Preparing data for AI analysis (ETL pipeline)

Module 3: Machine Learning Basics for SOC

• Overview of Supervised, Unsupervised, and Reinforcement Learning
• Choosing the right ML model for SOC tasks
• Common algorithms: Decision Trees, Random Forest, K-Means, SVM
• Model training, testing, and evaluation (Precision, Recall, F1-score)

Module 4: AI for Threat Detection

• Anomaly detection vs. Signature-based detection
• Real-time alert generation using AI
• AI for detecting zero-day threats
• Case studies: AI vs. Advanced Persistent Threats (APT)

Module 5: Incident Classification & Prioritization

• Alert triage using AI
• Noise reduction in large-scale environments
• Correlation of multi-source alerts using AI
• Predicting alert severity levels

Module 6: AI for Automated Response

• Automated playbooks and workflows
• AI bots for Tier-1 analyst tasks
• Use of NLP in chatbots for incident communication
• Threat containment using AI-based triggers

Module 7: AI in Threat Intelligence Integration

• Threat Intelligence feeds (TI): what they are and how AI uses them
• AI-driven IOC (Indicators of Compromise) matching
• Building threat profiles with AI

Module 8: Predictive Threat Hunting

• Proactive threat hunting with AI
• Building behavior baselines
• Visualizing attack paths with AI tools
• Detecting insider threats

Module 9: Hands-on Projects and Use Cases

• Building an AI-based SOC dashboard
• Case study: AI identifying a ransomware attack
• Creating a mini-SOC with open-source tools (like ELK Stack + ML)
• Simulated incident response with automation

Module 10: Ethical & Security Concerns

• Bias in AI algorithms
• Data privacy and compliance issues (GDPR, HIPAA)
• Model poisoning and adversarial attacks
• Logging and accountability of AI decisions

Final Assessment / Capstone Project

• Design a mini AI-SOC architecture
• Train a model to detect specific cyber threats
• Build an alert triaging system using AI logic