Applied AI for Digital Forensics and Incident Response
Digital forensics and incident response (DFIR) produces more data than an analyst can read, but the evidence usually cannot be sent to a public AI service. This focused course in Toronto teaches you to use local large language models on your own hardware.
You will speed up analysis, summarisation, and artifact correlation while keeping the process forensically sound. The recurring question is how to let a model assist without breaking the explainability an investigation depends on.
- Duration
- 4 weeks
- Format
- Live online, instructor-led
- Prerequisites
- None required
- Class size
- Capped at 14 learners
What you will be able to do
- Run a large language model locally so evidence never leaves
- Summarise and triage large evidence sets faster
- Correlate artifacts across sources with model assistance
- Keep an AI-assisted process forensically sound and explainable
- Recognize where a model may hallucinate and verify accordingly
- Document how AI was used so findings survive challenge
Course outline
4 modules
- Evidence that cannot go to a public service
- Privacy, PIPEDA, and chain of custody considerations
- What a local model can and cannot do
- Setting up an offline analysis workstation
What you need before you start
- You have DFIR experience or have taken a forensics or IR course
- You are comfortable working on the command line
- You do not need prior AI experience
- You accept that a model assists but never replaces analyst judgement
Who this course is for
- DFIR analysts drowning in evidence volume
- Incident responders who cannot use cloud AI on case data
- Forensic examiners curious about safe AI assistance
- SOC staff who support investigations
Questions about this course
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