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SEC595Cyber Defense, Artificial Intelligence
SEC595: Applied Data Science and AI/Machine Learning for Cybersecurity Professionals
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AI-FOCUSEDNew

FOR563: Applied AI for Digital Forensics and Incident Response: Leveraging Local Large Language Models

FOR563Digital Forensics and Incident Response, Artificial Intelligence
  • 1 Day (Instructor-Led)
  • 6 Hours (Self-Paced)
Course authored by:
Mari DeGrazia
Mari DeGrazia
Register Now
FOR509: Enterprise Cloud Forensics and Incident Response
Course authored by:
Mari DeGrazia
Mari DeGrazia
Register Now
  • 6 CPEs

    Apply your credits to renew your certifications

  • Self-paced

    Train at your own pace from wherever you are

  • Intermediate Skill Level

    Course material is geared for cyber security professionals with hands-on experience

  • 4 Hands-On Lab(s)

    Apply what you learn with hands-on exercises and labs

Jump to:

FOR563 teaches DFIR professionals to harness private, local AI using Large Language Models (LLMs) for secure, hands-on investigation and analysis at scale.

Course Overview

FOR563 empowers DFIR professionals to integrate AI by teaching how to run private, local Large Language Models (LLMs) without exposing sensitive data to third parties. This one-day, hands-on course moves beyond generic use cases to show how LLMs can be used in a forensics and incident response context: building custom agents, analyzing logs, forensics artifacts, and databases with natural language, and fine-tuning models for specialized investigations. While focused on local models, the techniques taught can be adapted for cloud or API-based LLMs. You'll gain practical skills you can immediately apply to real-world DFIR workflows, using AI effectively and at scale.

This course is now open for pre-sale! Pre-register today to save 10% and gain early access starting February 3. Use code PRESALE10 at checkout.

What You'll Learn

  • Configure and deploy local Large Language Models (LLMs) through both GUI and programmatic methods
  • Build and implement custom AI agents for forensic and incident response use cases
  • Analyze structured data—including logs, text messages, and databases—using natural language
  • Fine-tune LLMs for specialized DFIR tasks using custom datasets

Business Takeaways

  • Reduce the risk of data exposure by enabling AI-assisted analysis without sending sensitive data to third-party cloud services
  • Improve incident response workflows by equipping DFIR teams to use natural language interfaces for analysis
  • Lower reliance on proprietary AI platforms by training analysts to deploy and manage local, self-hosted Large Language Models (LLMs)
  • Expand investigative capabilities with custom AI agents tailored to your organization's specific forensic needs
  • Accelerate adoption of AI-driven workflows without compromising internal security or compliance requirements
  • Support knowledge retention and skill development by standardizing repeatable, scalable AI-driven forensic processes
  • Future-proof DFIR operations by preparing staff for integrating AI solutions across evolving data sources and forensic tooling

Meet Your Authors

Mari DeGrazia
Mari DeGrazia

Mari DeGrazia

Certified Instructor

Mari DeGrazia loves the satisfaction of solving a good puzzle. That fascination paired with her technical abilities has made digital forensics the perfect career fit. She has 20 years of experience in the IT industry, including 10 years in DFIR.

Read more about Mari DeGrazia

Course Syllabus

Explore the course syllabus below to view the full range of topics covered in FOR563: Applied AI for Digital Forensics and Incident Response: Leveraging Local Large Language Models.

Section 1Applied AI for Digital Forensics and Incident Response

Section 1 introduces students to the real-world application of Large Language Models (LLMs) in digital forensics and incident response. Students will learn how to deploy local models, build custom agents, and analyze structured forensic data using natural language, all in a private, GPU-backed environment.

Topics covered

  • Introduction to LLMs and their use in DFIR workflows
  • Model setup, local hosting, and parameter tuning
  • Structured artifact analysis (logs, JSON, mobile data, binary config files)
  • Creating and using LLM-powered forensic agents
  • Natural language querying at scale

Labs

  • Setting up a GPU-enabled lab environment for local model use
  • Querying and analyzing forensic data with local LLMs
  • Building and customizing DFIR-focused AI agents
  • Exploring the basics of LLM fine-tuning

Things You Need To Know

Course Schedule & Pricing

Looking for Group Purchasing Options?Contact Us
  • Location & instructor

    Virtual (OnDemand)

    Instructed by
    Mari DeGrazia
    Date & Time
    OnDemand (Anytime)Self-Paced, 4 months access
    Course price
    $995 USD*Prices exclude applicable local taxesBuy now for access on Feb 3. Use code Presale10 for 10% off course price!
    Registration Options
    Self-Paced
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