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Module 3: AI-Driven Threat Defense & Response

Overview

This module explores how artificial intelligence and machine learning are transforming both cyber threats and defenses. Students will learn to leverage AI for threat detection, hunting, and automated response while understanding the emerging landscape of AI-powered attacks.

Learning Duration

[To be determined]

Prerequisites

• Basic understanding of security operations
• Familiarity with threat detection concepts
• Knowledge of incident response fundamentals

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Topics

3.1 Understanding Agentic AI Threats: Automated Attacks and Defense

Key Concepts:

• AI-powered attack techniques
• Autonomous offensive tools
• Adversarial machine learning
• AI defense mechanisms

Content Areas:

• AI-driven vulnerability discovery
• Automated exploit generation
• Polymorphic malware and evasion techniques
• Adversarial attacks on ML models
• AI-powered social engineering
• Defensive AI strategies

[Detailed content to be added]

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3.2 Building an Autonomous SOC (Security Operations Center)

Key Concepts:

• Modern SOC architecture
• AI/ML in threat detection
• Alert triage automation
• Integration ecosystems

Content Areas:

• SOC maturity models
• Machine learning for anomaly detection
• Automated alert correlation
• SIEM and XDR platforms
• AI-powered threat intelligence
• False positive reduction strategies

[Detailed content to be added]

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3.3 Threat Hunting with GenAI Tools

Key Concepts:

• Proactive threat hunting
• GenAI-assisted analysis
• Hypothesis-driven hunting
• Pattern recognition

Content Areas:

• Threat hunting methodologies (TaHiTI, MITRE)
• Using GenAI for hypothesis generation
• Natural language query interfaces
• Log analysis and correlation
• Indicators of Compromise (IoC) discovery
• Threat actor behavior analysis

[Detailed content to be added]

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3.4 Automated Incident Response (SOAR) Workflows

Key Concepts:

• Security Orchestration, Automation, and Response
• Playbook development
• Automated remediation
• Tool integration

Content Areas:

• SOAR platform capabilities
• Incident response playbook design
• Automated containment strategies
• Integration with security tools (EDR, SIEM, firewalls)
• Workflow orchestration
• Measuring SOAR effectiveness (MTTR, MTTD)

[Detailed content to be added]

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Hands-on Labs

Lab 1: AI-Powered Threat Detection

Objective: [To be added] Duration: [To be added] Steps: [To be added]

Lab 2: Threat Hunting Exercise

Objective: [To be added] Duration: [To be added] Steps: [To be added]

Lab 3: Building SOAR Playbooks

Objective: [To be added] Duration: [To be added] Steps: [To be added]

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Case Studies

Case Study 1: AI-Driven SOC Transformation

Challenge: [To be added] Solution: [To be added] Results: [To be added]

Case Study 2: SOAR Implementation Success

Challenge: [To be added] Solution: [To be added] Results: [To be added]

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Assessment

Quiz Questions

• [To be added]

Project Assignment

Title: Design an AI-Enhanced Threat Detection System Description: [To be added] Deliverables: [To be added]

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Resources

Required Reading

• MITRE ATT&CK Framework
• [AI security research papers]
• [To be added]

Recommended Tools

• SIEM: Splunk, Elastic Security, Microsoft Sentinel
• XDR: Cortex XDR, SentinelOne, CrowdStrike
• SOAR: Palo Alto XSOAR, Splunk SOAR, Swimlane
• Threat Hunting: Velociraptor, OSQuery, Jupyter Notebooks

Further Learning

• [AI/ML security courses]
• [Threat hunting certifications]
• [SOAR training resources]

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Last Updated: 2026-01-07