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chore(ms-ai-architect): scrub identifying references from fixtures + remove screenshots

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Removes:
- All 6 PNG screenshots (playground/screenshots/) and the capture script
  (scripts/screenshots/capture-playground.py).
- "Screenshots" section from plugin README.
- "Screenshot-suite" section from plugin CLAUDE.md.
- Screenshots bullet from marketplace root README's ms-ai-architect listing.

Scrubs the 17 synthetic fixtures + CHANGELOG/CLAUDE/README of identifying
references: organization names, government-agency names, agency-specific
terminology, sector-specific use cases. Replaced with generic placeholder
data ("Acme AS" / "Demosystem") that exercises the same parser archetypes.

Plugin's domain-target wording (Datatilsynet, offentlig sektor, offentlig
myndighet, rettshåndhevelse, NS 5814, Utredningsinstruksen, EU AI Act
Annex III categories) is intact — those describe the plugin's intended
audience, not any specific entity.

This is a cleanup commit. Earlier git history still contains the prior
references; force-push or rebase is required if scrubbing the history is
desired. That decision is out of scope here — please run it separately
if needed.

Verified post-scrub:
- bash tests/validate-plugin.sh -> 215/215 PASS
- bash tests/run-e2e.sh --playground -> 240/240 PASS (170 + 70)
This commit is contained in:
Kjell Tore Guttormsen 2026-05-03 20:53:49 +02:00
commit e57dee5a03
28 changed files with 89 additions and 469 deletions
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14
plugins/ms-ai-architect/playground/test-fixtures/review.md
View file

@ -1,22 +1,22 @@
# Arkitekturgjennomgang — ANPR-trafikkanalyse
# Arkitekturgjennomgang — Demosystem
System: ANPR-trafikkanalyse (Statens vegvesen)
System: Demosystem (Acme AS)
Vurderingsdato: 2026-04-30
Reviewers: AI-arkitekt, sikkerhetsarkitekt, personvernombud
Reviewers: AI-arkitekt, sikkerhetsarkitekt, Datatilsynet
## Funn
| ID | Severity | Lokasjon | Anbefaling |
|----|----------|----------|------------|
| F-01 | critical | Authentication layer | Tilgang til AI-forklaringer mangler attribute-based access control — alle saksbehandlere ser alle saker. Implementer ABAC basert på sak-tildeling. |
| F-01 | critical | Authentication layer | Tilgang til AI-forklaringer mangler attribute-based access control — alle saksbehandler ser alle saker. Implementer ABAC basert på sak-tildeling. |
| F-02 | high | Data pipeline | Treningsdata oppdateres månedlig, men ingen formell drift-deteksjon. Etabler statistisk drift-monitoring i Azure Monitor. |
| F-03 | high | Model serving | Modellen serves fra en enkelt regional endpoint uten failover. Replikér til en sekundær region for RTO < 1t. |
| F-04 | high | Logging | Audit-logg lagres 30 dager — under arkivlovens krav for sak-relevant info. Endre retensjon til 7 år for sak-knyttede oppslag. |
| F-05 | medium | Cost management | Ingen budsjettalarmer på Azure AI Services — prediction-kostnaden kan øke med 4× ved trafikk-topp uten varsel. |
| F-05 | medium | Cost management | Ingen budsjettalarmer på Azure AI Services — prediction-kostnaden kan øke med 4× ved belastnings-topper uten varsel. |
| F-06 | medium | Compliance | FRIA-rapport ikke vedlikeholdt etter modell-endring 2026-03-12. Re-evaluering trengs. |
| F-07 | medium | UX | Saksbehandler-grensesnitt viser ikke konfidensgrad tydelig nok — risiko for over-trust på AI-output. |
| F-07 | medium | UX | saksbehandler-grensesnitt viser ikke konfidensgrad tydelig nok — risiko for over-trust på AI-output. |
| F-08 | low | Documentation | README mangler oppdatert arkitekturdiagram (siste fra 2025-11). |
| F-09 | low | Testing | Manglende E2E-test for utenlandske kjennemerker. |
| F-09 | low | Testing | Manglende E2E-test for utenlandske objekt-ID. |
## Sammendrag