check-governance

name	check-governance
description	Run the governance guardrail regression suite (56 tests, ~9s, no LLM calls). Covers all 5 governance dimensions — injection categories, evasion layers, PII/credential leakage, manipulation signals, sovereignty. Optionally re-check live governance_signals.json for one or all corpus architectures. Use after any change to chatbot/harness/governance.py to confirm no regressions.
allowed-tools	Bash(python3:*) Bash(source:*) Bash(pytest:*)

check-governance — Governance Guardrail Regression

Runs the full governance test suite without any LLM calls or network access. Covers injection detection (9 categories), evasion layers (base64, char-spacing, typoglycemia), PII/credential leakage, manipulation signals, sovereignty checks.

Run

# Full regression suite (~9s, no LLM, no network)
cd "$(git rev-parse --show-toplevel)" && source .venv/bin/activate \
  && python3 .claude/skills/check-governance/scripts/check-governance.py

# Check live governance_signals.json for one architecture
cd "$(git rev-parse --show-toplevel)" && source .venv/bin/activate \
  && python3 .claude/skills/check-governance/scripts/check-governance.py 21_agentic_ai_system

# Check all corpus architectures that have governance_signals.json
cd "$(git rev-parse --show-toplevel)" && source .venv/bin/activate \
  && python3 .claude/skills/check-governance/scripts/check-governance.py --all

What it checks

Unit tests (always run)

Test class	Coverage
TestCleanInputs	All 5 dimensions LOW on benign input
TestExploitation	Injection node, path traversal, oversized label
TestDataLeakage	NRIC, email, phone, credential keywords
TestManipulation	Fallback synthesis, divergence, confidence swing
TestSovereignty	Cross-boundary nodes, ZDR inference edges
TestInjectionCategories	9 named categories, injection_categories dict
TestEvasionLayers	Char-spacing, dot-spacing, base64, typoglycemia
TestNoFalsePositivesOnSecurityControls	Security control labels → no FP

Live signal check (when arch or --all passed)

For each architecture, loads governance_signals.json from report/ and reports:

• Exploitation severity and any injection categories found
• Leakage flags (PII, credential keywords)
• Manipulation signals (confidence swing, divergence)
• Sovereignty flags (cross-boundary nodes, ZDR signals)
• Overall risk level

Output example

Governance Regression — tests/test_harness_governance.py
  56 passed  2 xfailed  0 failed   (9.1s)

Live Signal Check — 21_agentic_ai_system
  exploitation:   LOW  — no injection patterns
  leakage:        LOW  — no PII indicators
  manipulation:   MEDIUM  — confidence_swing=16.78, divergence=0
  sovereignty:    MEDIUM  — zdr_signals: inference→external: LLMGateway → Anthropic
  overall_risk:   MEDIUM

When to run

• After any edit to chatbot/harness/governance.py
• After updating MITRE ATLAS data (new injection technique names)
• At session start when working on the governance or QualityStage pipeline

Related skills

• /quick-test — broader integration sanity (MITRE load, API key, embeddings)
• /aivss-score — AIVSS scoring deep-dive for a specific architecture
• /aivss-gate — show gate thresholds and last SIEM event summary