AI DFIR Foundations: Investigating AI, LLMs, and RAG Systems Fundamentals Training by Tonex
Modern incident response increasingly involves AI components—models, prompts, retrieval chains, and the data trails they leave. This course equips practitioners to investigate AI-driven systems with the same rigor applied to traditional breaches. You’ll learn how to reconstruct model behavior, analyze prompts and jailbreak attempts, and trace RAG pipelines across indexes, embeddings, and caches. Impact on cybersecurity is direct: better triage, faster scoping, and more precise containment when AI is abused or fails. Teams leave prepared to interpret AI logs, validate evidence, and report defensible findings that stand up to scrutiny.
Learning Objectives
- Understand AI DFIR scope across models, prompts, and RAG
- Reconstruct model behavior from artifacts and telemetry
- Perform prompt/jailbreak forensics and risk scoring
- Examine embeddings, vector stores, and retrieval chains
- Build repeatable AI incident triage workflows
- Strengthen cybersecurity by detecting, scoping, and preventing AI-enabled threats
Audience
- DFIR Analysts
- SOC Teams
- AI Engineers
- Incident Responders
- Threat Hunters
- Cybersecurity Professionals
Course Modules
Module 1 – AI DFIR Frameworks
- DFIR for AI scope
- Evidence taxonomy
- Chain-of-custody basics
- AI threat classes
- Model lifecycle risks
- Reporting standards
Module 2 – Model Behavior Reconstruction
- Inference trace capture
- Temperature/top-k impacts
- System vs user prompts
- Tool/function call trails
- Determinism vs variance
- Reproducibility methods
Module 3 – Prompt and Jailbreak Forensics
- Prompt change history
- Safety policy mapping
- Jailbreak pattern library
- Prompt injection paths
- Guardrail evasion triage
- Payload risk scoring
Module 4 – RAG Memory and Embeddings
- Corpus provenance checks
- Chunking/window effects
- Embedding drift analysis
- Vector store integrity
- Retrieval path auditing
- Cache poisoning signs
Module 5 – AI Log Analysis and Triage
- Unified log schema
- Session stitching steps
- Anomaly baselining
- Alert enrichment rules
- Triage decision trees
- Escalation criteria
Module 6 – Containment and Reporting
- Kill-switch strategies
- Config rollback plans
- Scope/impact statements
- Evidentiary snapshots
- Executive-ready briefs
- Post-incident hardening
Ready to upskill your team for AI-era investigations? Join Tonex to master practical AI DFIR—from prompts and model behavior to RAG pipelines and log-driven triage—so you can detect, contain, and report AI incidents with confidence.