# certsocietegenerale/IRM

Incident Response Methodologies 2022

Repository: https://github.com/certsocietegenerale/IRM
Canonical: https://ross.abutalabs.com/products/irm
License: NOASSERTION
License Family: other
Last push: 2025-04-11T11:11:13+00:00

## Health v2 (maintenance only)
Score: 39/100 (v2, computed 2026-09-02T17:46:02.011165+00:00)
- activity 16, release rhythm 35, longevity 98
- inputs: {"age_days": 1372, "days_push": 509, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases, no_license
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 1140, forks 196 (observed 2026-08-28T04:03:44.346800+00:00)

## What it is
A collection of operational incident response cheat sheets (IRM-2022) published by CERT Societe Generale with CERT aDvens. Each methodology covers a specific type of security incident a CERT team handles, providing step-by-step handling best practices.

## Use cases
- respond to a security incident step by step
- build a CSIRT incident handling playbook
- train SOC analysts on incident triage
- standardize malware outbreak response procedures
- reference cheat sheets during DFIR engagements
- translate incident response procedures for a French-speaking team

## When to choose
- you need proven, operational incident response procedures from an active CERT
- you want ready-made cheat sheets covering common incident types
- you are building or improving a CSIRT/SOC playbook library

## When to avoid
- you need executable tooling or automation rather than documentation
- you require permissive licensing for redistribution beyond CC BY 3.0 terms
- you need vendor-specific or compliance-driven IR frameworks

## Facets
- artifact type: learning-resource
- maturity: active
- function: documentation, security
- domain: security, documentation, developer-tools
- platform: cross-platform
- tags: incident-response, cheat-sheets, dfir, soc, best-practices, cybersecurity

## Member repositories
- certsocietegenerale/IRM (main) score 39

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:03:44.346800+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-30T06:35:29.036586+00:00, confidence not recorded.
  - readme: https://github.com/certsocietegenerale/IRM (fetched 2026-08-28T04:03:44.346800+00:00, sha e89811fe2bef)
- Data as of 2026-08-30T08:39:29.467469+00:00.