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rsyslog/doc/ai/README.md
Rainer Gerhards 9b466b89d1 doc: add security triage rubric for AI agents 
Why: automated security reviews need a shared proof standard so
hardening opportunities are not over-stated as confirmed vulnerabilities.

Impact: contributor-facing guidance now distinguishes confirmed issues,
potential issues, hardening, and invalid findings before severity or CWE
language is used.

Before/After: agent guidance pointed at documentation structure, but did
not define a security-finding evidence bar; the new rubric documents the
required source, reachability, sink, missing guard, and impact checks.

Technical Overview:
Add doc/ai/security_triage_rubric.md with classification, proof, CWE,
severity, rsyslog-specific, test, wording, and inline-comment guidance.
Link the rubric from the root AGENTS.md and doc/ai AGENTS.md files.
List the new file in doc/ai/README.md and doc/Makefile.am so it is easy
to discover and packaged with the documentation support files.

With the help of AI-Agents: Codex
2026-05-10 15:39:10 +02:00

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rsyslog AI Knowledge Base (KB)

Purpose: A compact, upload-ready knowledge base that reflects the current rsyslog docs structure and rules.

Audience: Contributors and AI assistants generating/maintaining docs.

What's here

File Purpose
structure_and_paths.md Directory layout and naming conventions
authoring_guidelines.md Required blocks, tone, section order
mermaid_rules.md Diagram syntax rules
terminology.md Canonical rsyslog vocabulary
security_triage_rubric.md Security finding proof, severity, and hardening classification rules
chunking_and_embeddings.md RAG extraction schema and chunk structure
crosslinking_and_nav.md Navigation and cross-reference patterns
drift_monitoring.md Detecting doc/code drift
module_map.yaml Module paths and locking hints
templates/ RST templates for concept, tutorial, and module pages

RAG Knowledge Base

The documentation build generates a machine-readable RAG dataset at build/rag/rsyslog_rag_db.json. This is produced by ../build_rag_db.py and contains ~12,000 structured chunks for AI retrieval pipelines.

Regenerate with: make -C doc json-formatter

See chunking_and_embeddings.md for schema details.

Current canonical terms

  • Log pipeline (internally also called message pipeline).
  • Use getting_started/beginner_tutorials/ (no learning_path/).

Last reviewed: 2025-12-23