ms-ai-architect/skills/ms-ai-security/references/ai-security-engineering/data-leakage-prevention-ai.md

# Data Leakage Prevention in AI Contexts

**Category:** AI Security Engineering
**Last updated:** 2026-06-19 | Verified: MCP 2026-06-19
**Målgruppe:** Enterprise AI architects og security teams
**Type:** reference
**Source:** https://learn.microsoft.com/security/benchmark/azure/mcsb-v2-artificial-intelligence-security
**Status:** Established Practice

## Innhold

- [Oversikt](#oversikt)
- [1. Prompt Context Isolation](#1-prompt-context-isolation)
- [2. Model Extraction Defense](#2-model-extraction-defense)
- [3. Membership Inference Protection](#3-membership-inference-protection)
- [4. DLP Policy Enforcement Across AI Workloads](#4-dlp-policy-enforcement-across-ai-workloads)
- [5. Cache Security Management](#5-cache-security-management)
- [6. Praktiske Arkitekturmønstre](#6-praktiske-arkitekturmønstre)
- [7. Compliance & Audit](#7-compliance--audit)
- [8. Tooling & Automation](#8-tooling--automation)
- [9. Monitoring & Detection](#9-monitoring--detection)
- [10. Anbefalinger for Cosmo Skyberg](#10-anbefalinger-for-cosmo-skyberg)
- [For Cosmo Skyberg](#for-cosmo-skyberg)

## Oversikt

Data leakage prevention (DLP) i AI-sammenheng omfatter beskyttelse mot utilsiktet eller ondsinnet eksponering av sensitiv informasjon gjennom AI-modeller, prompts, og responses. Dette dokumentet dekker Microsoft-plattformens verktøy og mønstre for å forhindre datalekkasje i tre kritiske lag: prompt context isolation, model extraction defense, og membership inference protection.

**Sentrale risikoer:**
- **Prompt-basert lekkasje:** Brukere injiserer sensitiv informasjon i prompts som deretter prosesseres eller lagres ukontrollert
- **Model extraction:** Angripere bruker API-tilgang til å reverse-engineere proprietære modeller
- **Membership inference:** Angripere deduserer om spesifikke data var i training set
- **Cache leakage:** Sensitiv informasjon eksponeres via delte cacher eller prompt history
- **Response leakage:** AI-modeller avslører PII, IP, eller confidential data i svar

## 1. Prompt Context Isolation

### 1.1 Microsoft Purview DLP for Microsoft 365 Copilot

**Konsept:** Prevent Copilot from processing sensitive prompts in real-time ved å blokkere prompts som inneholder sensitive information types (SITs).

**Kapabiliteter:**
- **Prompt scanning:** Deep content inspection av user prompts før prosessering
- **Sensitive information type (SIT) detection:** Deteksjon av kredittkortnummer, personnummer, passporter, etc.
- **Real-time blocking:** Forhindrer Copilot i å returnere svar når prompts inneholder sensitiv data (action: `Processing prompts`, preview)
- **Web search blocking (GA):** Egen action `Performing Web Searches` blokkerer ekstern web-søk som grounding-kilde når prompt inneholder SITs — Copilot svarer fortsatt fra interne M365-kilder brukeren har tilgang til *(Verified MCP 2026-06-19)*
- **External email blocking (preview):** Betingelsen `Email is received from > External users` ekskluderer ekstern e-post fra grounding/summarisering/citation som prompt injection-vern; kun avsender-metadata evalueres, ikke e-postkroppen *(Verified MCP 2026-06-19)*

**Policy configuration:**

```powershell
# Eksempel: Blokkerer norske personnummer og kredittkortnummer i Copilot-prompts
New-DlpCompliancePolicy `
  -Name "Copilot Prompt Protection" `
  -Comment "Prevents sensitive data in prompts" `
  -Locations "[{\"Workload\":\"Applications\",\"Location\":\"470f2276-e011-4e9d-a6ec-20768be3a4b0\",\"Inclusions\":[{Type:\"Tenant\", Identity:\"All\"}]}]" `
  -EnforcementPlanes @("CopilotExperiences") `
  -Mode Enable

New-DlpComplianceRule `
  -Name "Block Norway SSN in Prompts" `
  -Policy "Copilot Prompt Protection" `
  -ContentContainsSensitiveInformation @{Name="Norway National Identity Number"; MinCount="1"} `
  -RestrictAccess @(@{setting="ProcessingPrompts";value="Block"}) `
  -NotifyUser Owner `
  -NotifyPolicyTipDisplayOption "Dialog"
```

**Støttede lokasjoner:** *(Verified MCP 2026-04)*
- Microsoft 365 Copilot og Copilot Chat (inkludert pre-built agents)
- Copilot in Word, Excel, PowerPoint
- Policy location er kun tilgjengelig i **Custom**-policymalen
- Alle andre lokasjoner i policyen deaktiveres når denne lokasjonen velges

**Begrensninger:**
- Kan ikke kombinere "Content contains sensitive info types" og "Content contains sensitivity labels" i samme regel
- Policy-oppdateringer tar opptil 4 timer å tre i kraft
- Admin units støttes ikke
- DLP kan ikke scanne innholdet i filer som lastes opp direkte i prompts — kun prompt-teksten selv evalueres *(Verified MCP 2026-04)*

**Brukeropplevelse:**
Når en bruker forsøker å sende en prompt med blokkert SIT, vises en melding: *"The request can't be completed because it contains sensitive information that the organization has blocked Microsoft 365 Copilot from using."*

### 1.2 Sensitivity Label-basert Blocking

**Konsept:** Prevent Copilot from processing files and emails med spesifikke sensitivity labels i response summaries.

**Use case eksempel:**
Organisasjonen har labels "Highly Confidential", "Confidential", "Internal", "Public", "Personal". De ønsker å ekskludere "Personal" og "Highly Confidential" fra Copilot-prosessering for å oppfylle GDPR og compliance-krav.

```powershell
# Hent label GUID
Get-Label | Format-List Priority,ContentType,Name,DisplayName,Identity,Guid

$guidHighlyConfidential = "e222b65a-b3a8-46ec-ae12-00c2c91b71c0"
$guidPersonal = "d4f28ae4-9c5e-4e7f-bf4a-5e3d6f1a7c8b"

$loc = "[{\"Workload\":\"Applications\",\"Location\":\"470f2276-e011-4e9d-a6ec-20768be3a4b0\",\"Inclusions\":[{Type:\"Tenant\", Identity:\"All\"}]}]"

New-DLPCompliancePolicy -Name "Copilot Sensitivity Label Policy" -Locations $loc -EnforcementPlanes @("CopilotExperiences")

$advRule = @{
  "Version" = "1.0"
  "Condition" = @{
    "Operator" = "And"
    "SubConditions" = @(
      @{
        "ConditionName" = "ContentContainsSensitiveInformation"
        "Value" = @(
          @{
            "groups" = @(
              @{
                "Operator" = "Or"
                "labels" = @(
                  @{name = $guidHighlyConfidential; type = "Sensitivity"},
                  @{name = $guidPersonal; type = "Sensitivity"}
                )
                "name" = "Default"
              }
            )
          }
        )
      }
    )
  }
} | ConvertTo-Json -Depth 100

New-DLPComplianceRule -Name "Exclude Confidential Content" -Policy "Copilot Sensitivity Label Policy" -AdvancedRule $advRule -RestrictAccess @(@{setting="ExcludeContentProcessing";value="Block"})
```

**Støttede filtyper:** *(Verified MCP 2026-04)*
- File items (stored og actively open): Word (.docx/.docm), Excel (.xlsx/.xlsm/.xlsb), PowerPoint (.pptx/.ppsx), og PDF-filer (ved aktivert PDF-støtte)
- Emails sent on or after January 1, 2025
- Kun filer i SharePoint Online og OneDrive for Business
- Labels med bruker-definerte tillatelser støttes nå for search, DLP og eDiscovery (kun nyopplastede/redigerte filer)

**Begrensninger:**
- Calendar invites støttes ikke
- Når en fil med blokkert label er åpen i Word/Excel/PowerPoint, disables skills i disse appene

**Resultat:**
Identified items vises fortsatt i citations, men innholdet brukes ikke i response eller tilgang av Copilot.

## 2. Model Extraction Defense

### 2.1 Outbound URL Restriction (Azure AI Services DLP)

**Konsept:** Begrens hvilke outbound URLs Azure OpenAI og Azure AI Services kan aksessere for å forhindre at modeller ekfiltrerer data eller lekker model weights til unauthorized endpoints.

**Risikoreduksjon:**
- Forhindrer model extraction via API calls til attacker-controlled servers
- Blokkerer data exfiltration via tool calls eller plugin interactions
- Reduserer supply chain risk ved å whiteliste kun trusted endpoints

**Konfigurasjon (Azure CLI):**

```bash
# Aktiver restrictOutboundNetworkAccess
az rest -m patch \
  -u /subscriptions/{subscription-id}/resourceGroups/{resource-group}/providers/Microsoft.CognitiveServices/accounts/{account-name}?api-version=2024-10-01 \
  -b '{"properties": { "restrictOutboundNetworkAccess": true, "allowedFqdnList": [ "contoso.com", "api.trustedpartner.com" ] }}'
```

**Konfigurasjon (PowerShell):**

```powershell
$patchParams = @{
  ResourceGroupName = 'myresourcegroup'
  ResourceProviderName = 'Microsoft.CognitiveServices'
  ResourceType = 'accounts'
  Name = 'myaccount'
  ApiVersion = '2024-10-01'
  Payload = '{"properties": { "restrictOutboundNetworkAccess": true, "allowedFqdnList": [ "contoso.com", "api.trustedpartner.com" ] }}'
  Method = 'PATCH'
}
Invoke-AzRestMethod @patchParams
```

**Viktige detaljer:**
- Maksimum 1000 URLs i `allowedFqdnList`
- Støtter fully qualified domain names (FQDN)
- Tar opptil 15 minutter før oppdatert liste trer i kraft

**Støttede tjenester:**
- Azure OpenAI
- Microsoft Foundry (Foundry-based projects)
- Azure Vision
- Content Moderator
- Custom Vision
- Face API
- Document Intelligence
- Speech Services
- QnA Maker

### 2.2 Network Security Perimeter (NSP)

**Konsept:** Implementer network security perimeter for å begrense inbound og outbound access til Azure OpenAI og Foundry-baserte prosjekter.

**Implementering:**
- [Add network security perimeter to Azure OpenAI](https://learn.microsoft.com/en-us/azure/foundry-classic/openai/how-to/network-security-perimeter)
- [Add Foundry to a network security perimeter](https://learn.microsoft.com/en-us/azure/foundry/how-to/add-foundry-to-network-security-perimeter)

**Kombiner med:**
- Azure Private Link for network-level data isolation
- Azure RBAC for workload og user group access control
- Microsoft Entra ID for centralized authentication

### 2.3 Model Integrity Monitoring

**Konsept:** Detect model drift og unauthorized modifications som kan indikere extraction attempts eller supply chain compromise.

**Tilnærming:**
- **Digital signatures:** Verifiser model files med hash verification
- **Versioning:** Store models i Azure Blob Storage med versioning enabled
- **Audit trails:** Log alle model-related activities (registration, deployment, access) i Azure Monitor
- **Automated scanning:** Integrate security validation pipelines som scanner for embedded backdoors

**Azure Machine Learning Model Registry:**

```bash
# Eksempel: Deploy centralized model registry med RBAC
az ml model register \
  --name "my-verified-model" \
  --model-path "azureml://..." \
  --description "Verified model with signature" \
  --tags "verified=true" "hash=sha256:abc123..."
```

**Monitoring:**

```kusto
// Azure Monitor KQL: Detect unauthorized model access
AzureDiagnostics
| where ResourceType == "MICROSOFT.MACHINELEARNINGSERVICES/WORKSPACES"
| where OperationName == "ModelDownload"
| where Identity_claim_upn_s !in ("authorized-user@contoso.com")
| project TimeGenerated, Identity_claim_upn_s, ResourceId, OperationName
```

## 3. Membership Inference Protection

### 3.1 Differential Privacy

**Konsept:** Apply differential privacy techniques for å forhindre at angripere kan dedusere om specific data points var i training set.

**Microsoft SmartNoise:**
Microsoft co-developed SmartNoise, et open-source differential privacy system.

**Repository:** [https://github.com/opendifferentialprivacy/smartnoise-core](https://github.com/opendifferentialprivacy/smartnoise-core)

**Use case:**
- Fine-tuning på sensitive datasett (healthcare, financial)
- Trening av custom models med PII
- Compliance med GDPR Article 25 (data protection by design)

**Integration med Azure Machine Learning:**

```python
from opendp.smartnoise.sql import PandasReader, PrivateReader
import pandas as pd

# Load sensitive data
df = pd.read_csv("sensitive_data.csv")
reader = PandasReader(df, metadata)

# Apply differential privacy to query
private_reader = PrivateReader(reader, privacy=Privacy(epsilon=1.0))
result = private_reader.execute("SELECT AVG(age) FROM data")
```

**Privacy budget management:**
- Epsilon (ε): Lavere verdi = høyere privacy, lavere accuracy
- Delta (δ): Probability of privacy breach
- Anbefaling: ε ≤ 1.0 for high-sensitivity data

### 3.2 Encryption at Rest & In Transit

**Data at rest:**
- **FIPS 140-2 compliant 256-bit AES encryption** for all Azure OpenAI data
- **Customer-Managed Keys (CMK)** via Azure Key Vault for fine-tuned models og training data
- **Microsoft-managed keys** som default (transparent encryption)

**Data in transit:**
- **TLS encryption** for all traffic mellom Databricks og model partners
- **Zero data retention endpoints** for Partner-powered AI assistive features
- **Azure Private Link** for network-level isolation

**CMK configuration:**

```bash
# Enable customer-managed key for Azure OpenAI
az cognitiveservices account update \
  --name myopenai \
  --resource-group myresourcegroup \
  --encryption KeyVaultKeyId=https://myvault.vault.azure.net/keys/mykey/version
```

**Key rotation:**
- Rotate keys ved defined schedule eller ved key compromise
- Audit key usage via Azure Key Vault diagnostics

### 3.3 Training Data Provenance

**Konsept:** Maintain non-repudiable data provenance records for å verifisere at kun authorized data ble brukt i training.

**Confidential AI med Azure Confidential Computing:**
- **Attestation:** Data providers autoriserer bruk av datasets for spesifikke tasks (verified by attestation)
- **Confidential training:** Data forblir protected i use via Trusted Execution Environments (TEEs)
- **Provenance records:** Generate non-repudiable logs av data/model lineage

**Bruk:**
- Medical diagnosis models (HIPAA compliance)
- Financial risk assessment (SOX, PCI-DSS)
- Business analysis med corporate IP

## 4. DLP Policy Enforcement Across AI Workloads

### 4.1 Multi-Layered Content Filtering

**Konsept:** Implement filtering på tre lag: input, internal processing, output.

**Layer 1: Input filtering**
- **Azure AI Content Safety (Prompt Shield):** Scan user inputs for attack patterns (hate speech, violence, adversarial inputs)
- **Azure API Management:** Enforce rate-limiting, schema validation, authentication policies
- **Data format validation:** Reject malformed inputs

**Layer 2: Internal processing validation**
- **Azure Machine Learning model monitoring:** Track intermediate outputs, detect anomalies during inference
- **Azure Defender for Cloud:** Scan runtime environments for adversarial behavior
- **Robustness testing:** Validate behavior under adversarial conditions

**Layer 3: Output filtering**
- **Azure AI Content Safety:** Block harmful responses (bias, non-compliant content)
- **Validation logic:** Cross-check outputs mot organizational policies via Azure Functions
- **Logging:** Log all inputs/outputs i Azure Monitor for traceability

**Eksempel-arkitektur:**

```
User Prompt
    ↓
[Azure API Management] → Rate-limit, Auth, Schema Validation
    ↓
[Prompt Shield] → Detect malicious patterns
    ↓
[Azure OpenAI] → Process prompt
    ↓
[AML Model Monitoring] → Detect anomalies
    ↓
[Content Safety Output Filter] → Block harmful content
    ↓
[Azure Functions Validator] → Cross-check policies
    ↓
[Azure Monitor] → Log interaction
    ↓
Response to User
```

### 4.2 Endpoint DLP for Third-Party AI

**Konsept:** Prevent sensitive data leakage to third-party generative AI sites (ChatGPT, Claude, etc.) via browser-based interactions.

**Microsoft Purview Endpoint DLP:**
- **Windows onboarding:** Onboard Windows computers til Microsoft Purview
- **Policy enforcement:** Block eller warn users from pasting sensitive information i third-party AI sites
- **Supported actions:** Block paste, block upload, warn with override

**Eksempel:**
User forsøker å paste kredittkortnummer til ChatGPT → Purview Endpoint DLP blokkerer action eller viser warning.

**Konfigurere:**

```powershell
New-DlpCompliancePolicy -Name "Block AI Site Data Leak" -ExchangeLocation All

New-DlpComplianceRule `
  -Name "Block Credit Card to ChatGPT" `
  -Policy "Block AI Site Data Leak" `
  -ContentContainsSensitiveInformation @{Name="Credit Card Number"; MinCount="1"} `
  -BlockAccess $true `
  -NotifyUser Owner
```

**Supported platforms:** Windows computers med Endpoint DLP agent installed.

### 4.3 Insider Risk Management for AI Interactions

**Konsept:** Detect risky AI use via machine learning-based anomaly detection.

**Microsoft Purview Insider Risk Management:**
- **Risky interaction detection:** Attempted prompt injection, use of sensitive data
- **Adaptive protection:** Block high-risk users from accessing sensitive content via Copilot
- **Alerts:** Real-time alerts for policy violations

**Policy templates:**
- "DSPM for AI - Detect risky AI usage"
- "DSPM for AI - Unethical behavior in AI apps"
- "DSPM for AI - Protect sensitive data from Copilot processing"

**One-click policies fra DSPM for AI (classic):**

```powershell
# Aktiveres via Microsoft Purview portal → DSPM for AI → Recommendations
```

## 5. Cache Security Management

### 5.1 Prompt History Isolation

**Konsept:** Prevent shared caches eller prompt history fra å eksponere sensitive information på tvers av brukere eller sesjoner.

**Microsoft 365 Copilot:**
- **User context isolation:** Prompts kjører i security context av bruker som initierer prompt
- **Permission enforcement:** Brukere ser kun items de har permissions til
- **No cross-user cache leakage:** Copilot deler ikke data mellom users

### 5.2 Azure OpenAI Prompt Caching

**Konsept:** Azure OpenAI støtter ikke persistent prompt caching på tvers av users. Hver API call er stateless (med mindre conversation history sendes eksplisitt i request).

**Sikkerhet:**
- **Stateless API:** Ingen automatisk deling av prompts mellom users
- **Token usage logging:** Log all token usage for audit purposes
- **Customer-controlled retention:** Customers kontrollerer retention av conversation history

### 5.3 Databricks Assistant Cache Protection

**DatabricksIQ Trust & Safety:**
- **No training on user data:** Databricks does not train foundation models med data submitted to features
- **No cross-customer data sharing:** Data ikke brukt for å generere suggestions for andre customers
- **Zero data retention (model partners):** Partner-powered AI features bruker zero data retention endpoints
- **Data residency controls:** DatabricksIQ-powered features comply med data residency boundaries (Geos)

## 6. Praktiske Arkitekturmønstre

### 6.1 Defense-in-Depth for AI Leakage Prevention

**Lag 1: Network isolation**
- Azure Private Link
- Network Security Perimeter
- VNet integration

**Lag 2: Identity & Access** *(Verified MCP 2026-04)*
- Microsoft Entra ID RBAC
- Managed Identity (for sikker autentisering uten lagrede credentials — per CAF Secure AI)
- Separation of duties (developers, reviewers, operators)
- Virtual networks for isolering av AI-kommunikasjonskanaler

**Lag 3: Data protection**
- Microsoft Purview DLP (prompt + file/email blocking)
- Sensitivity labels (automatic inheritance)
- Data classification (PII, financial, IP)

**Lag 4: Model security**
- Model registry med approval workflows
- Automated security scanning (hash verification, backdoor detection)
- Version control i Azure Storage med versioning

**Lag 5: Runtime protection**
- Azure AI Content Safety (Prompt Shield + Output Filter)
- Azure Defender for AI Services (threat detection)
- AML Model Monitoring (drift detection, anomaly detection)

**Lag 6: Audit & Compliance**
- Microsoft Purview Audit (unified audit log for AI activities)
- Azure Monitor (centralized logging)
- Activity explorer (DSPM for AI)

### 6.2 Azure OpenAI + Purview DLP Reference Architecture

```
┌─────────────────────────────────────────────────────────────────┐
│                         User (M365 Copilot)                    │
└─────────────────────────────────────────────────────────────────┘
                                ↓
┌─────────────────────────────────────────────────────────────────┐
│               Microsoft Purview DLP Policy Engine               │
│  - Scan prompt for SITs (credit card, SSN, etc.)                │
│  - Check file sensitivity labels                                │
│  - Block processing if policy match                             │
└─────────────────────────────────────────────────────────────────┘
                                ↓ (if allowed)
┌─────────────────────────────────────────────────────────────────┐
│                    Microsoft 365 Copilot                        │
│  - Entra ID RBAC (user context isolation)                       │
│  - Grounding på SharePoint/OneDrive (permission-enforced)       │
└─────────────────────────────────────────────────────────────────┘
                                ↓
┌─────────────────────────────────────────────────────────────────┐
│                     Azure OpenAI Service                        │
│  - Private endpoint (NSP)                                        │
│  - Outbound URL restriction (DLP)                               │
│  - CMK encryption at rest                                        │
│  - TLS in transit                                                │
└─────────────────────────────────────────────────────────────────┘
                                ↓
┌─────────────────────────────────────────────────────────────────┐
│                    Azure AI Content Safety                      │
│  - Output filter (harmful content)                              │
│  - Validation against org policies                              │
└─────────────────────────────────────────────────────────────────┘
                                ↓
┌─────────────────────────────────────────────────────────────────┐
│                    Microsoft Purview Audit                      │
│  - Log prompt, response, referenced files                       │
│  - Activity explorer (DSPM for AI)                              │
└─────────────────────────────────────────────────────────────────┘
```

### 6.3 Enterprise AI Gateway Pattern

**Konsept:** Centralize all AI traffic gjennom Azure API Management som AI Gateway. Azure API Management kan nå også sikre Model Context Protocol (MCP) server-endepunkter. *(Verified MCP 2026-04)*

**Fordeler:**
- **Unified security policies:** Enforce authentication, DLP, rate-limiting på ett sted
- **Traffic monitoring:** Log all API usage for audit
- **Cost control:** Track token usage per team/project
- **Model versioning:** Route requests til ulike model versions basert på policy
- **MCP endpoint security:** Deploy Azure API Management for å sikre MCP server-endepunkter (ny kapabilitet) *(Verified MCP 2026-04)*

**Arkitektur:**

```
Applications
    ↓
[Azure API Management (AI Gateway)]
  - Entra ID authentication
  - Rate-limiting (TPM, RPM)
  - DLP policy enforcement (allowedFqdnList check)
  - Token usage logging
    ↓
[Azure OpenAI] or [Custom Models] or [Copilot Studio]
```

**Configuration:**

```bash
# Deploy API Management med managed identity
az apim create \
  --name myaigateway \
  --resource-group myresourcegroup \
  --publisher-email admin@contoso.com \
  --publisher-name Contoso \
  --sku-name Developer

# Integrate med Entra ID
az apim api create \
  --resource-group myresourcegroup \
  --service-name myaigateway \
  --api-id openai-api \
  --path "/openai" \
  --display-name "Azure OpenAI Gateway" \
  --service-url "https://myopenai.openai.azure.com" \
  --protocols https \
  --subscription-required true
```

## 7. Compliance & Audit

### 7.1 Unified Audit Log for AI Activities

**Microsoft Purview Audit:**
- **Captured events:** Prompts, responses, referenced files, sensitivity labels
- **Context:** User, timestamp, service, files accessed
- **Retention:** Configurable (90 days to 10 years)

**Query AI activities:**

```powershell
# Search unified audit log for Copilot activities
Search-UnifiedAuditLog -StartDate (Get-Date).AddDays(-7) -EndDate (Get-Date) -Operations "CopilotInteraction"
```

**Activity Explorer (DSPM for AI):**
- Visual dashboard for AI interactions
- Filter by user, sensitivity label, SIT, time range
- Export for compliance reporting

### 7.2 Data Security Posture Management (DSPM) for AI

**Capabilities:**
- **Data risk assessments:** Identify oversharing risks
- **Recommendations:** "Protect your data from potential oversharing risks"
- **One-click policies:** Deploy DLP policies direkte fra recommendations
- **Compliance Manager integration:** Map controls til regulatory templates (GDPR, HIPAA, etc.)

**Rollout:**
- **DSPM for AI (classic):** Generally available
- **DSPM (preview):** New version med enhanced AI activities tab

### 7.3 Regulatory Compliance Mapping

| Regulation | Relevant DLP Controls | Microsoft Purview Tools |
|------------|----------------------|-------------------------|
| **GDPR Art. 25** | Data protection by design, minimize data processing | Sensitivity labels, DLP for Copilot, Differential Privacy |
| **HIPAA** | Protect PHI in AI interactions | DLP rules for PHI SITs, CMK encryption, Confidential AI |
| **PCI-DSS** | Protect cardholder data | DLP rules for credit card SITs, Outbound URL restriction |
| **SOX** | Protect financial records | Sensitivity labels (Highly Confidential), Audit logs |
| **CCPA** | Protect consumer personal data | DLP rules for California SITs, Data residency controls |
| **AI Act (EU)** | Risk management, transparency | DSPM for AI, Audit logs, Model provenance |

## 8. Tooling & Automation

### 8.1 PowerShell Module: ExchangePowerShell

**Viktige cmdlets:**
- `New-DlpCompliancePolicy`: Create DLP policy
- `New-DlpComplianceRule`: Add rule til policy
- `Get-DlpCompliancePolicy`: List policies
- `Set-DlpPolicy`: Update existing policy
- `Get-Label`: List sensitivity labels med GUIDs

**Installer:**

```powershell
Install-Module -Name ExchangeOnlineManagement
Connect-IPPSSession
```

### 8.2 Azure CLI Extensions

```bash
# Cognitive Services DLP
az cognitiveservices account show -g myresourcegroup -n myaccount
az rest -m patch -u /subscriptions/.../accounts/myaccount?api-version=2024-10-01 -b '{...}'

# Monitor AI activities
az monitor activity-log list --resource-group myresourcegroup --resource-type "Microsoft.CognitiveServices/accounts"
```

### 8.3 GitHub Samples

**Microsoft Purview API integration:**
- **Sample:** [serverless-chat-langchainjs-purview](https://github.com/Azure-Samples/serverless-chat-langchainjs-purview)
- **Use case:** Integrate Entra-registered AI app med Purview APIs for DLP enforcement

**Counterfit (AI security testing):**
- **Repository:** [https://github.com/Azure/counterfit/](https://github.com/Azure/counterfit/)
- **Use case:** Simulate cyberattacks mot AI systems for å validere DLP controls

**PyRIT (Python Risk Identification Toolkit):**
- **Repository:** [https://azure.github.io/PyRIT/](https://azure.github.io/PyRIT/)
- **Use case:** Red teaming av AI systems for prompt injection, jailbreak, data exfiltration testing

## 9. Monitoring & Detection

### 9.1 Microsoft Defender for AI Services

**Capabilities:**
- **AI threat protection:** Detect prompt injection, model manipulation, jailbreak attempts
- **Continuous monitoring:** Monitor model inference, API calls, plugin interactions
- **Integration:** Azure Sentinel for SIEM correlation med MITRE ATLAS og OWASP LLM Top 10

**Deployment:**

```bash
az security pricing create \
  --name "AI" \
  --tier "Standard" \
  --resource-group myresourcegroup
```

### 9.2 Anomaly Detection for AI Workloads

**Azure AI Anomaly Detector:**
- **Metrics:** API request patterns, model confidence scores, token usage
- **Alerts:** Unusual spikes i API calls, unexpected model outputs, irregular data access

**KQL query for anomaly detection:**

```kusto
AzureDiagnostics
| where ResourceType == "MICROSOFT.COGNITIVESERVICES/ACCOUNTS"
| where OperationName == "Inference"
| summarize RequestCount = count() by bin(TimeGenerated, 1h), CallerIpAddress
| where RequestCount > 1000  // Threshold
| project TimeGenerated, CallerIpAddress, RequestCount
```

### 9.3 Alerting & Incident Response

**Azure Monitor Alerts:**

```bash
az monitor metrics alert create \
  --name "High Token Usage Alert" \
  --resource-group myresourcegroup \
  --scopes "/subscriptions/.../providers/Microsoft.CognitiveServices/accounts/myopenai" \
  --condition "total TokensUsed > 100000" \
  --window-size 5m \
  --evaluation-frequency 1m \
  --action-group "/subscriptions/.../actionGroups/ai-security-team"
```

**Incident response workflow:**
1. **Alert triggered** (e.g., suspected data exfiltration)
2. **Azure Sentinel** → Correlate med threat intelligence
3. **Purview Audit** → Retrieve prompt/response logs
4. **Block user** → Via Adaptive Protection (Insider Risk Management)
5. **Rotate keys** → If API key compromise suspected
6. **Post-incident review** → Update DLP policies

## 10. Anbefalinger for Cosmo Skyberg

### For Azure OpenAI

1. **Alltid enable outbound URL restriction** (`restrictOutboundNetworkAccess: true`) med whitelisted FQDNs
2. **Bruk Private Link + NSP** for production deployments
3. **Enable CMK encryption** hvis fine-tuning på sensitive data
4. **Log all API calls** til Azure Monitor med minimum 90 days retention

### For Microsoft 365 Copilot

1. **Deploy DLP policies for prompts** (SIT detection) og files/emails (sensitivity labels)
2. **Kombiner med Sensitivity Labels** — auto-classify data, inherit protection
3. **Enable Insider Risk Management** for risky AI interaction detection
4. **Bruk DSPM for AI** for continuous posture assessment

### For Custom AI Applications

1. **Implement AI Gateway** (Azure API Management) for unified security
2. **Multi-layered content filtering** (input → processing → output)
3. **Integrate Purview APIs** for DLP enforcement i custom apps
4. **Red team regularly** med PyRIT, Counterfit, Azure AI Red Teaming Agent

### For Compliance & Audit

1. **Enable Unified Audit Log** for alle AI services
2. **Map DLP policies til regulations** (GDPR, HIPAA, PCI-DSS, etc.)
3. **Use Activity Explorer** for visual analysis av AI interactions
4. **Document decisions** i ADRs når du velger DLP strategy

### Security Checklist

- [ ] Outbound URL restriction enabled på Azure OpenAI?
- [ ] DLP policy for Copilot prompts (SITs) deployed?
- [ ] DLP policy for Copilot files/emails (sensitivity labels) deployed?
- [ ] Private Link + NSP configured?
- [ ] CMK encryption enabled for fine-tuned models?
- [ ] Unified Audit Log enabled (90+ days retention)?
- [ ] Insider Risk Management policies active?
- [ ] AI Gateway (APIM) deployed med rate-limiting + auth?
- [ ] Multi-layered content filtering (Azure AI Content Safety)?
- [ ] Red teaming plan established (quarterly)?
- [ ] Incident response runbook documented?

## For Cosmo Skyberg

**Når bruke dette:**
- Kunde spør om "hvordan forhindre datalekkasje i AI-løsninger"
- Compliance-krav (GDPR, HIPAA) krever DLP for AI workloads
- Security assessment avdekker risiko for prompt injection eller model extraction
- Enterprise AI deployment trenger defense-in-depth strategi

**Praktisk tilnærming:**
1. **Start med risikovurdering:** Hvilke data er mest sensitive? Hvilke leakage vectors er mest sannsynlige?
2. **Prioriter quick wins:** Deploy Microsoft Purview DLP for Copilot (prompts + files) — får immediate risk reduction
3. **Bygg lag-for-lag:** Network isolation → Data protection → Model security → Runtime monitoring
4. **Automatiser enforcement:** Bruk one-click policies fra DSPM for AI
5. **Valider med red teaming:** Kjør PyRIT/Counterfit før production rollout

**Kombiner med andre kunnskapsfiler:**
- `prompt-injection-defense-mechanisms.md` — For input validation strategies
- `jailbreak-prevention-strategies.md` — For output filtering og behavioral controls
- `ai-threat-modeling.md` — For systematic risk identification
- `rag-security-patterns.md` — For grounding data protection (når det finnes)
- `azure-ai-services/document-intelligence-security.md` — For PII redaction i documents (når det finnes)

**Typisk arkitekturanbefaling:**
> "For å beskytte mot datalekkasje anbefaler jeg en multi-layered tilnærming:
> 1. **Prompt-nivå:** Microsoft Purview DLP for å blokkere sensitive SITs i Copilot-prompts.
> 2. **Model-nivå:** Outbound URL restriction på Azure OpenAI + Private Link for network isolation.
> 3. **Output-nivå:** Azure AI Content Safety for å filtrere harmful/non-compliant responses.
> 4. **Audit-nivå:** Unified Audit Log + DSPM for AI for continuous monitoring.
> Dette gir defense-in-depth med både preventive, detective, og corrective controls."

**Microsoft Learn kilder:**
- [Microsoft Purview DLP for Copilot](https://learn.microsoft.com/en-us/purview/dlp-microsoft365-copilot-location-learn-about)
- [Azure AI Services DLP](https://learn.microsoft.com/en-us/azure/ai-services/cognitive-services-data-loss-prevention)
- [Secure AI (Cloud Adoption Framework)](https://learn.microsoft.com/en-us/azure/cloud-adoption-framework/scenarios/ai/secure) — Verified MCP 2026-04: Bekrefter bruk av Microsoft Purview DLP for AI-workflows, content filtering for å forhindre sensitiv informasjonslekkasje, og Purview Insider Risk Management for prompt-basert data exfiltration-deteksjon og identifisering av risikofull AI-atferd.
- [Artificial Intelligence Security (MCSB)](https://learn.microsoft.com/en-us/security/benchmark/azure/mcsb-v2-artificial-intelligence-security)
- [Confidential AI](https://learn.microsoft.com/en-us/azure/confidential-computing/confidential-ai)