Lion Cipher Lab
Triage a batch of security alerts with a local LLM — safely
- AI Security
- Intermediate
- ~40 min
- Free · no account required
Why it matters
A SOC analyst can face thousands of alerts a shift, most of them noise. A local LLM can read a messy batch in seconds and hand back a ranked, plain-language first pass — which alert looks like the real incident, which are benign — so a human spends their attention where it matters. That is a genuine force multiplier. But an LLM will also state wrong things with total confidence and will happily leak whatever you paste into it. The skill that makes this safe — and employable — is knowing exactly when to lean on the model and when you must not, and always checking its story against the raw…
What you'll learn
- Using a local LLM to speed alert triage
- Prompt structure for reliable summarization
- Data-handling discipline (never feed secrets/PII to a model)
- Verifying model output against source evidence
- Reading the OWASP LLM risks and NIST AI RMF in practice
Steps in this lab
- When an LLM helps you triage — and when it must not
- Install Ollama and pull a small model (local, free, no key)
- Build a SYNTHETIC alert batch — never use real data
- Write a triage prompt that actually gets good results
- Verify every claim against the raw alerts — this is the job
- The guardrails, and when to reach for this at all — the recap