InfosecTrain’s Certified AI Powered SOC Analyst Certification Training

• Module 1: Introduction to SOC
  • What is a SOC?
    • Definition, role in cybersecurity defense
    • SOC structures: Centralized, Distributed, Virtual SOCs
  • SOC Analyst Roles
    • L1: Monitoring, triage, escalation
    • L2 & L3: Deep investigation, threat hunting, forensics
  • Key SOC Functions
    • Log monitoring, alert triage, threat detection, incident response
  • SOC Maturity Model
    • From reactive → proactive → predictive SOC
  • Common SOC Tools
    • SIEM, EDR/XDR, Threat Intel Platforms, Open-Source Security Analyst oriented models
• Module 2: Introduction to AI for Cybersecurity
  • What is AI?
    • AI vs ML vs DL vs NLP vs LLM
    • Predictive AI vs Generative AI
  • Why AI in Cybersecurity?
    • Reducing false positives, handling large datasets, automated response
  • AI in SOC – Use Cases
    • Log summarization, phishing detection, anomaly detection
    • AI-driven report generation, automated playbooks
  • AI Limitations in SOC
    • Hallucinations, bias, explainability, data privacy concerns
  • Open-Source and Free-Tier AI Tools
    • Free Tier proprietary LLMs, Ollama, LMStudio, Hugging Face models
• Lab:
  • Run an LLM locally (Phi-3 Mini / Mistral via Ollama) → ask it to summarize sample Windows Event logs and
    classify alerts.
• Module 3: Network Security & Threat Landscape
  • Basics of Networking for SOC
    • OSI model, TCP/IP, ports & protocols
    • Common attacks (DDoS, brute force, phishing, ransomware)
    • Case studies of famous attacks
  • Threat Intelligence
    • Threat intelligence types
    • IOC (Indicators of Compromise)
    • MITRE ATT&CK for SOC Analysts
  • AI in Threat Intel
    • Using AI to summarize threat feeds
    • AI-assisted correlation of IOCs
• Lab:
  • Gathering Threat Intel feeds using AI.
  • Capture sample PCAP in Wireshark → use Python + AI model to identify anomalies
• Module 4: AI in Vulnerability Management & Assessment
  • Vulnerability Management Basics
    • What is a vulnerability? CVE, CVSS, exploitability
    • VM lifecycle: Scan → Assess → Prioritize → Remediate → Report
    • Tools overview: Nessus (pro), OpenVAS (free), Nmap + NSE scripts
    • SOC Analyst role vs. vulnerability management team
  • AI in Vulnerability Assessment
    • AI for CVE explanation: simplify technical CVEs into analyst-friendly notes
    • AI for prioritization: map severity + exploitability + asset criticality
    • AI for remediation recommendations: patch, config change, or mitigation
    • AI in report drafting for management/non-technical audience
• Lab:
  • OpenVAS/NMAP Scan + AI Explanation
  • AI-Generated Vulnerability Report
• Module 5: SIEM & AI-Assisted Log Analysis
  • SIEM Fundamentals
    • Architecture, log sources, parsing, correlation rules
    • Popular tools: Splunk, ELK, Microsoft Sentinel
  • Challenges in Log Analysis
    • High volume, repetitive patterns
  • AI Integration
    • AI for log summarization and anomaly detection
    • ChatGPT prompt engineering for SIEM queries
    • AI-driven “Explain this log” and “Generate query”
• Lab:
  • AI-powered analysis of Windows Event Logs (4624, 4625, 4670, etc.)
  • Using AI to generate Splunk queries and summarize alerts
  • Parse Suricate logs to elk via filebeat and use AI model to detect network Attack (Malicious Log
    detector to generate alerts)
• Module 6: Phishing, Malware, and Insider Threats
  • Phishing
    • Types (email, smishing, vishing, spear phishing, whaling)
    • Real case studies (Norfund, Colonial Pipeline)
  • Malware
    • Introduction to Malware
    • Types of Malware
    • Malware Family Naming
    • Behavioral detection vs signature-based detection
  • Insider Threats
    • Privilege misuse, data exfiltration patterns
  • AI in Detection
    • AI-based phishing email detection
    • AI chatbot for suspicious email reporting
    • AI in malware recognition
• Lab:
  • AI-based phishing email classification
  • AI for static malware analysis
• Module 7: Incident Response with AI
  • IR Lifecycle
    • Preparation → Detection → Containment → Eradication → Recovery → Lessons Learned
  • AI in IR
    • AI-guided playbooks
    • Automating IOC enrichment (IP/URL/domain lookups)
    • AI-assisted RCA (Root Cause Analysis)
• Lab:
  • Using AI to Assist in Phishing Incident Response
  • Network Traffic Analysis using Wireshark + AI

• Aspiring SOC Analysts (L1)
• Cybersecurity beginners entering SOC roles and aiming to use AI tools effectively
• Junior Security Analysts working with logs and alerts
• IT professionals transitioning into SOC operations
• Fresh graduates aiming for entry-level SOC positions

• Basic understanding of networking & cybersecurity fundamentals
• Familiarity with Windows/Linux basics
• No prior SOC or AI experience required

You will be able to:

• Build foundational SOC analysis skills with AI
• Use AI for alert triage, log summaries and investigations
• Detect phishing, malware and anomalies with AI support
• Automate vulnerability reporting and IOC enrichment
• Assist in IR workflows using AI models
• Enhance SOC productivity with AI-driven tools