chore(ms-ai-architect): refresh KB high-bucket — 49 files [skip-docs]

KB-currency refresh (high priority, 2026-06-19) via /architect:kb-update.
49 high-prioritets governance/security/monitoring-filer re-verifisert mot
Microsoft Learn (MCP) — delegert til 8 parallelle Opus-subagenter gruppert
etter delt kilde, verifisert i hovedkontekst (diff-review + tester).

Hovedendringer (faktuelle korreksjoner + currency):
- MITRE ATLAS-IDer korrigert (supply-chain): AML.T0050 -> AML.T0018.000
  (Poison AI Model); AML.T0020 = Poison Training Data; T1195 Supply Chain
  Compromise. Gamle IDer var utdaterte (verifisert mot MCSB v2 AI-1).
- OTel-sampling presisert (distributed-tracing): adaptive sampling = klassisk
  App Insights SDK; OTel-distroen sampler IKKE by default (fixed-rate/
  rate-limited maa konfigureres); Functions parent-based sampling er default.
- MCSB v2 AI-kontroller AI-1 -> AI-7 (risk-taxonomy three-pillar, scoring-
  framework, rubrics, red-team, adversarial); Defender for Cloud AI threat
  protection + AI-SPM (GA).
- AI gateway (APIM) multi-provider: Anthropic Messages API v2-tiers, Google
  Vertex, unified model API (preview), MCP/A2A, Foundry-integrasjon; eksakte
  policy-navn (llm-emit-token-metric maks 5 dims, llm-semantic-cache-*,
  score-threshold = avstand, MS-eks. 0.15).
- Purview Enterprise AI apps inkl. Anthropic Claude (Enterprise) + ChatGPT
  Enterprise; Security Dashboard for AI (Agent 365-inventar, MCP-servere,
  tredjepartsmodeller; Security Reader minimumsrolle).
- Entra Agent ID: CA-lisenskrav (Entra ID P1/P2 + Agent 365), CA-scoping per
  tilgangsmoenster (on-behalf-of/app-only/agent-as-user), CA-grenser,
  connector-permissions som API-permissions.
- Copilot DLP: Block SITs in web search (GA, Performing Web Searches) + Block
  external email (preview) som prompt injection-vern.
- Azure AI Language PII: tre feature-typer, GA-API 2026-05-01; NOIdentityNumber
  bekreftet dedikert kategori for norske foedselsnummer.
- Foundry Tools-rename forsterket paa tvers; alle 49 Last updated -> 2026-06-19.

Discovery: 500 kandidater (alle Databricks-stoey) -> kun registry-kandidater,
ingen nye skills/-filer -> 389-telling uendret. validate 239 PASS,
kb-integrity 115/115 (262 orphan-warnings uendret), gitleaks clean.

Co-Authored-By: Claude Opus 4.8 (1M context) <noreply@anthropic.com>
Claude-Session: https://claude.ai/code/session_01REiKFhP4w6xGXXqWKpPCJJ

skills/ms-ai-security/references/ai-security-engineering/supply-chain-security-ai-models.md

@@ -1,7 +1,7 @@
 # Supply Chain Security for AI Models and Dependencies
 
 **Kategori:** AI Security Engineering
-**Dato:** 2026-02-05
+**Dato:** 2026-06-19
 **Relatert plattform:** Azure AI Foundry, Azure Machine Learning, Azure DevOps, Microsoft Defender for Cloud
 
 ---
@@ -217,15 +217,15 @@ deployment = ManagedOnlineDeployment(
 
 ### Angrepsvektorer
 
-**Backdoor ML (MITRE ATT&CK: AML.T0050)**:
-- Malicious MLaaS provider trojaner modell med trigger som aktiverer ved deployment
+**Poison AI Model (MITRE ATLAS: AML.T0018.000)**:
+- Malicious MLaaS provider trojaner modell ved å manipulere modellvekter med trigger som aktiverer ved deployment
 - Eksempel: Modell klassifiserer virus som "benign" når spesifikt filnavn inkluderes
 
-**Compromise Model Supply Chain (AML.T0020)**:
-- Adversary uploader poisoned models til public marketplaces (HuggingFace Hub, Caffe Model Zoo)
+**Supply Chain Compromise (T1195)**:
+- Adversary uploader poisoned models til public marketplaces (HuggingFace Hub, Caffe Model Zoo), eller kompromitterer biblioteker/datasett
 - Modeller inneholder embedded logic som exfiltrerer data eller manipulerer outputs
 
-**Data Poisoning (AML.T0022)**:
+**Poison Training Data (AML.T0020)**:
 - Malicious data injisert under pre-training, fine-tuning, eller embedding
 - Eksempel: SQL injection i scrapet dataset → modell lærer å returnere falske resultater
 
@@ -521,9 +521,9 @@ Integrer SBOM-generering i deployment workflow:
 - [Vulnerability Management for Azure Machine Learning](https://learn.microsoft.com/en-us/azure/machine-learning/concept-vulnerability-management)
 - [Security planning for LLM-based applications](https://learn.microsoft.com/en-us/ai/playbook/technology-guidance/generative-ai/mlops-in-openai/security/security-plan-llm-application)
 
-### MITRE ATT&CK Framework
-- [AML.T0020: Compromise Model Supply Chain](https://atlas.mitre.org/techniques/AML.T0020)
-- [AML.T0050: Backdoor Model](https://atlas.mitre.org/techniques/AML.T0050)
+### MITRE ATT&CK / ATLAS (per MCSB v2 AI-1)
+- [AML.T0018.000: Poison AI Model](https://atlas.mitre.org/techniques/AML.T0018)
+- [AML.T0020: Poison Training Data](https://atlas.mitre.org/techniques/AML.T0020)
 - [T1195: Supply Chain Compromise](https://attack.mitre.org/techniques/T1195/)
 
 ### Compliance Mappings
@@ -539,5 +539,5 @@ Integrer SBOM-generering i deployment workflow:
 
 ---
 
-**Sist oppdatert**: 2026-02-05
-**Neste review**: 2026-05-05 (eller ved store endringer i Azure ML supply chain features)
+**Sist oppdatert**: 2026-06-19
+**Neste review**: 2026-09-19 (eller ved store endringer i Azure ML supply chain features)