PIR Lessons

PIR literacy — post-incident review, lessons learned, action items — on $HUNT_LAB closed incident stub.

PIR Lessons• Audio Walkthrough

00:00/02:58

Visual · t30_pir_lessons

PIR = named lessons-learned rows. $HUNT_LAB. Original Cyberlium.

Incidents teach if you capture lessons — name PIR structure on YOUR closed lab stub before repeating the same hunt misses.

Post-Incident Review (PIR) literacy names: review participants category, timeline reconstruction UTC, what worked / what failed rows, root cause category (misconfig, stolen creds category — literacy not blame), action items with owners, and lessons learned summary for detection/engineering. Analyst drafts PIR outline on $HUNT_LAB closed incident from Modules 6–8 — cites PICERL Lessons Learned stage — without blaming individuals inappropriately, without unauthorized prod PIR publication, without hiding containment delays. Cyberlium teaches constructive defender retrospective on lab stubs. Refused: public shaming in PIR, prod PIR without RoE, skipping action items. Lab row: PIR template (participants, timeline, worked/failed, actions) filled for one lab incident.

1. PIR structure named

Participants, timeline, worked/failed, root cause category, action items — five literacy sections.

PIR closes PICERL Lessons Learned — feeds detection gaps Module 9-2.

Command guide

Try these commands — PIR structure named

═══ TOOLS & WEBSITES ═══ Browse / read these (authorized learning only — stay in YOUR lab / program scope)

PIR template — https://csrc.nist.gov/publications/detail/sp/800-61/rev-2/final SANS post-incident — https://www.sans.org/white-papers/incident-handlers-handbook/ FIRST — https://www.first.org/resources/guides/

═══ INSTALL ═══

Linux (Debian/Ubuntu):

Command — copy this

sudo apt install python3

macOS:

Command — copy this

brew install python3

Windows: Download https://python.org/downloads/

═══ LINUX / macOS ═══

Command — copy this

export HUNT_LAB=${HUNT_LAB:-$HOME/cyberlium-lab/t30-hunt}
cat > "$HUNT_LAB/pir/pir-template-LAB-IR-001.md" <<'EOF'
# Post-Incident Review (PIR) — LAB-IR-001
## Incident summary
- Scenario ID: LAB-IR-001
- Timeline: see ir/ir-timeline-LAB-IR-001.md
- Impact: jsmith account compromise, C2 beacon (FAKE lab data)
## What happened
- Failed-then-success logon from 203.0.113.77
- winword→powershell execution chain
- Periodic HTTPS to 203.0.113.99
## What went well
- Hunt hypothesis validated across auth/process/netflow
- Correlation of HNT-001/002/003
## What needs improvement
- Detection gap: beacon ran 3 intervals before alert
- Tuning: add parent process allowlist exceptions
## Action items
- [ ] Deploy tuned sigma rule for rare parent
- [ ] Add dashboard panel for failed-then-success
- [ ] Tabletop exercise for containment decision tree
## Scope statement
- Authorized lab exercise only — YOUR seeded telemetry
EOF

Command — copy this

grep -E 'PIR|Action items|Scope' "$HUNT_LAB/pir/pir-template-LAB-IR-001.md"

Primary tools to practice this lesson: grep, python3. Reference sites: PIR template (https://csrc.nist.gov/publications/detail/sp/800-61/rev-2/final); SANS post-incident (https://www.sans.org/white-papers/incident-handlers-handbook/); FIRST (https://www.first.org/resources/guides/). Run every command in the box — install first, then the usage lines — only on YOUR lab / program scope.

2. Constructive tone

Focus on process and controls — not personal blame in lab notes.

Action items SMART at category level (owner, due stub, priority).

3. Refused

No unauthorized prod PIR publish; no fabricated lessons.

PIR literacy supports improvement — not cover-ups.

4. What you ship: PIR outline template

PIR five sections + one completed lab incident + NEVER blame-only line.

5. What you record before the next lesson

PIR outline template path.

6. Wrong vs right: stranger prod vs YOUR hunt telemetry

Worked failure — same MSF word, opposite target. Right never needs a café Wi-Fi or classmate laptop.

Wrong: Publish unauthorized prod PIR naming coworkers as 'at fault' on social media.
Right: PIR outline from $HUNT_LAB closed incident. Next: Detection Gaps.

Hands-on Exercise