LLMs for Cybersecurity Training in Kuala Lumpur, Malaysia

This training introduces cybersecurity professionals to the use of Large Language Models (LLMs) such as GPT, Claude, and others in threat analysis, alert triage, log analysis, security documentation, and automation of common security operations tasks. Participants will learn practical ways to integrate LLMs into cybersecurity workflows.

INTRODUCTION & CYBERSECURITY USE CASES

1. Introduction to LLMs in Cybersecurity

• Overview of LLMs and relevance in security operations
• Key LLM tools (GPT, Claude, Code Interpreter, etc.)
• Opportunities and limitations in a security context

2. Prompting for Cybersecurity Tasks

• Writing effective prompts for logs, alerts, and reports
• Extracting relevant data from large text or logs
• Ensuring accuracy and reducing hallucinations

3. Log and Alert Analysis with LLMs

• Parsing SIEM or system logs using AI
• Identifying anomalies and suspicious behavior
• Creating summarized threat snapshots from logs

4. Threat Intelligence and Summarization

• Using LLMs to summarize CVEs and threat reports
• Comparing vulnerability patterns
• Extracting IOC (Indicators of Compromise) with AI help

AUTOMATION & DOCUMENTATION WITH AI

1. AI-Powered Security Documentation

• Generating incident reports and summaries
• Writing security policies and SOPs with LLMs
• Creating knowledge base articles and FAQs

2. Vulnerability Assessment Assistance

• Using LLMs to describe and explain CVEs
• AI-driven remediation recommendations
• Generating executive summaries of vulnerabilities

3. Incident Response and Playbook Automation

• Generating and updating IR playbooks
• Role-based prompt templates (SOC analyst, blue team, etc.)
• Integrating LLMs into threat response workflows

4. Risk, Compliance & Security Awareness Content

• Drafting risk assessment summaries
• Generating compliance checklists (e.g., ISO, NIST, SOC2)
• Creating end-user awareness training scripts

5. Final Practice and Wrap-Up

• End-to-end AI workflow
• Group feedback and discussion
• Summary, best practices, and recommended tools