feat(ultraplan-local): v1.6.0 — /ultraresearch-local deep research command

Add /ultraresearch-local for structured research combining local codebase analysis with external knowledge via parallel agent swarms. Produces research briefs with triangulation, confidence ratings, and source quality assessment. New command: /ultraresearch-local with modes --quick, --local, --external, --fg. New agents: research-orchestrator (opus), docs-researcher, community-researcher, security-researcher, contrarian-researcher, gemini-bridge (all sonnet). New template: research-brief-template.md. Integration: --research flag in /ultraplan-local accepts pre-built research briefs (up to 3), enriches the interview and exploration phases. Planning orchestrator cross-references brief findings during synthesis. Design principle: Context Engineering — right information to right agent at right time. Research briefs are structured artifacts in the pipeline: ultraresearch → brief → ultraplan --research → plan → ultraexecute. Co-Authored-By: Claude Opus 4.6 <noreply@anthropic.com>
2026-04-08 08:58:35 +02:00 · 2026-04-08 08:58:35 +02:00 · baa2d0220b
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+# GenAI-Specific APIM Policies & Rules
+
+**Last updated:** 2026-02
+**Status:** GA
+**Category:** API Management & AI Gateway
+
+---
+
+## Introduksjon
+
+Azure API Management (APIM) inkluderer et sett med policyer spesifikt designet for generativ AI (GenAI). Disse policyene går utover tradisjonell API-gateway-funksjonalitet og adresserer unike utfordringer ved AI-workloads: content safety-modererering, prompt-validering, token-basert rate limiting, semantic caching, og audit-logging av prompts og completions. Samlet utgjør de kjernen i APIM sin AI gateway-kapabilitet.
+
+For norsk offentlig sektor er GenAI-spesifikke policyer kritisk viktige. Krav fra AI Act, Datatilsynet og NSM innebarer at AI-systemer må ha mekanismer for innholdssikkerhet, logging for etterprøvbarhet, og kontroll over hva slags innhold som genereres. APIM-policyer gir disse kontrollene uten at hver enkelt applikasjon må implementere dem selv — en sentralisert, konsistent tilnærming til AI governance.
+
+Denne referansen dekker alle GenAI-spesifikke APIM-policyer med fullstendige XML-eksempler, konfigurasjonsparametre og best practices. Policyene kan kombineres fritt i APIM sin inbound/outbound policy pipeline for å bygge en komplett AI safety-stack.
+
+---
+
+## Content Safety Integration
+
+### llm-content-safety Policy
+
+Policyen sender LLM-forespørsler til Azure AI Content Safety for moderering FØR de videresendes til backend-modellen:
+
+```xml
+<inbound>
+    <base />
+    <llm-content-safety backend-id="content-safety-backend"
+                        shield-prompt="true"
+                        enforce-on-completions="true">
+        <categories output-type="EightSeverityLevels">
+            <category name="Hate" threshold="4" />
+            <category name="Violence" threshold="4" />
+            <category name="SelfHarm" threshold="2" />
+            <category name="Sexual" threshold="2" />
+        </categories>
+        <blocklists>
+            <id>custom-blocklist-pii</id>
+            <id>custom-blocklist-org-specific</id>
+        </blocklists>
+    </llm-content-safety>
+</inbound>
+```
+
+### Prerequisites for Content Safety
+
+```bicep
+// 1. Azure AI Content Safety ressurs
+resource contentSafety 'Microsoft.CognitiveServices/accounts@2023-05-01' = {
+  name: 'content-safety-service'
+  location: 'westeurope'
+  kind: 'ContentSafety'
+  sku: { name: 'S0' }
+  properties: {
+    publicNetworkAccess: 'Disabled'
+  }
+}
+
+// 2. APIM Backend for Content Safety
+resource contentSafetyBackend 'Microsoft.ApiManagement/service/backends@2023-09-01-preview' = {
+  name: 'ai-gateway-apim/content-safety-backend'
+  properties: {
+    url: 'https://content-safety-service.cognitiveservices.azure.com'
+    protocol: 'http'
+    credentials: {
+      authorization: {
+        scheme: 'managed-identity'
+        parameter: 'https://cognitiveservices.azure.com'
+      }
+    }
+  }
+}
+```
+
+### Content Safety Konfigurasjon
+
+| Attributt | Beskrivelse | Standard |
+|-----------|-------------|---------|
+| `backend-id` | Backend-entitet for Content Safety | Obligatorisk |
+| `shield-prompt` | Sjekk for adversarial attacks (jailbreak) | `false` |
+| `enforce-on-completions` | Sjekk også respons fra modellen | `false` |
+
+### Kategorier og Terskelverider
+
+| Kategori | Beskrivelse | Anbefalt terskel (offentlig sektor) |
+|----------|-------------|-------------------------------------|
+| `Hate` | Hatefullt innhold, diskriminering | 2-4 (streng) |
+| `Violence` | Voldelig innhold | 2-4 (streng) |
+| `SelfHarm` | Selvskading | 2 (svært streng) |
+| `Sexual` | Seksuelt innhold | 2 (svært streng) |
+
+**Terskelskala:** 0 (mest restriktiv) til 7 (minst restriktiv). Lavere verdi = flere forespørsler blokkeres.
+
+### Severity Level Output Types
+
+| Output Type | Nivåer | Bruksområde |
+|------------|--------|------------|
+| `FourSeverityLevels` | 0, 2, 4, 6 | Standard, enklere policy |
+| `EightSeverityLevels` | 0-7 | Finkornet kontroll |
+
+### Blokkert Request-respons
+
+Når Content Safety blokkerer en forespørsel:
+
+```json
+{
+  "statusCode": 403,
+  "message": "Content safety violation detected. The request has been blocked."
+}
+```
+
+---
+
+## Prompt Validation Policies
+
+### Custom Prompt Validation
+
+Utover Azure AI Content Safety kan du implementere egne prompt-valideringsregler:
+
+```xml
+<inbound>
+    <base />
+
+    <!-- Valider prompt-lengde -->
+    <set-variable name="request-body" value="@{
+        return context.Request.Body.As<JObject>(preserveContent: true);
+    }" />
+
+    <choose>
+        <!-- Blokkér ekstremt lange prompts -->
+        <when condition="@{
+            var body = (JObject)context.Variables["request-body"];
+            var messages = body?["messages"] as JArray;
+            if (messages == null) return false;
+            var totalLength = messages.Sum(m => m["content"]?.ToString().Length ?? 0);
+            return totalLength > 50000;
+        }">
+            <return-response>
+                <set-status code="400" reason="Bad Request" />
+                <set-body>{
+    "error": {
+        "code": "PromptTooLong",
+        "message": "Total prompt length exceeds 50,000 characters."
+    }
+}</set-body>
+            </return-response>
+        </when>
+
+        <!-- Blokkér forespørsler uten system message -->
+        <when condition="@{
+            var body = (JObject)context.Variables["request-body"];
+            var messages = body?["messages"] as JArray;
+            if (messages == null) return true;
+            return !messages.Any(m => m["role"]?.ToString() == "system");
+        }">
+            <return-response>
+                <set-status code="400" reason="Bad Request" />
+                <set-body>{
+    "error": {
+        "code": "SystemMessageRequired",
+        "message": "A system message is required for all AI requests."
+    }
+}</set-body>
+            </return-response>
+        </when>
+
+        <!-- Blokkér forsøk på å overstyre system message -->
+        <when condition="@{
+            var body = (JObject)context.Variables["request-body"];
+            var messages = body?["messages"] as JArray;
+            if (messages == null) return false;
+            var systemMessages = messages.Where(m => m["role"]?.ToString() == "system").ToList();
+            return systemMessages.Count > 1;
+        }">
+            <return-response>
+                <set-status code="400" reason="Bad Request" />
+                <set-body>{
+    "error": {
+        "code": "MultipleSystemMessages",
+        "message": "Only one system message is allowed per request."
+    }
+}</set-body>
+            </return-response>
+        </when>
+    </choose>
+</inbound>
+```
+
+### Inject Mandatory System Prompt
+
+Tving en standard system prompt for alle forespørsler:
+
+```xml
+<inbound>
+    <base />
+
+    <!-- Injiser organisasjonens standard system prompt -->
+    <set-body>@{
+        var body = context.Request.Body.As<JObject>(preserveContent: true);
+        var messages = body["messages"] as JArray ?? new JArray();
+
+        // Organisasjonens mandatory system prompt
+        var orgSystemPrompt = new JObject {
+            ["role"] = "system",
+            ["content"] = "You are a helpful assistant for Statens vegvesen. " +
+                          "You must respond in Norwegian unless explicitly asked otherwise. " +
+                          "Never share personal data, internal processes, or confidential information. " +
+                          "Always cite sources when providing factual information."
+        };
+
+        // Fjern eksisterende system messages og legg inn organisasjonens
+        var userMessages = new JArray(messages.Where(m => m["role"]?.ToString() != "system"));
+        userMessages.Insert(0, orgSystemPrompt);
+        body["messages"] = userMessages;
+
+        return body.ToString();
+    }</set-body>
+</inbound>
+```
+
+---
+
+## Response Filtering
+
+### Filtrere Sensitiv Informasjon fra Responser
+
+```xml
+<outbound>
+    <base />
+
+    <!-- Fjern potensielle PII-lekkasjer fra AI-respons -->
+    <choose>
+        <when condition="@(!context.Response.Headers.GetValueOrDefault("Content-Type","")
+                           .Contains("text/event-stream"))">
+            <set-body>@{
+                var body = context.Response.Body.As<JObject>(preserveContent: true);
+                var choices = body?["choices"] as JArray;
+                if (choices == null) return body.ToString();
+
+                foreach (var choice in choices)
+                {
+                    var content = choice["message"]?["content"]?.ToString();
+                    if (content == null) continue;
+
+                    // Fjern fødselsnumre (11 siffer)
+                    content = System.Text.RegularExpressions.Regex.Replace(
+                        content, @"\b\d{11}\b", "[REDACTED-PII]");
+
+                    // Fjern e-postadresser
+                    content = System.Text.RegularExpressions.Regex.Replace(
+                        content, @"[\w.+-]+@[\w-]+\.[\w.-]+", "[REDACTED-EMAIL]");
+
+                    // Fjern telefonnumre (norsk format)
+                    content = System.Text.RegularExpressions.Regex.Replace(
+                        content, @"\b(\+47)?\s?\d{2}\s?\d{2}\s?\d{2}\s?\d{2}\b", "[REDACTED-PHONE]");
+
+                    choice["message"]["content"] = content;
+                }
+
+                return body.ToString();
+            }</set-body>
+        </when>
+    </choose>
+</outbound>
+```
+
+### Legg til Disclaimer i Responser
+
+```xml
+<outbound>
+    <base />
+    <set-header name="X-AI-Disclaimer" exists-action="override">
+        <value>AI-generated content. Verify information before use.</value>
+    </set-header>
+</outbound>
+```
+
+---
+
+## Rate Limiting per Model
+
+### Token Rate Limiting (llm-token-limit)
+
+Begrens token-forbruk per forbruker, per modell:
+
+```xml
+<inbound>
+    <base />
+
+    <!-- Global token-grense per subscription -->
+    <llm-token-limit
+        counter-key="@(context.Subscription.Id)"
+        tokens-per-minute="10000"
+        estimate-prompt-tokens="true"
+        remaining-tokens-variable-name="remainingTokens" />
+
+    <!-- Ekstra grense per modell -->
+    <choose>
+        <when condition="@(context.Request.MatchedParameters["deployment-id"] == "gpt-4o")">
+            <llm-token-limit
+                counter-key="@("gpt4o-" + context.Subscription.Id)"
+                tokens-per-minute="5000"
+                estimate-prompt-tokens="true" />
+        </when>
+        <when condition="@(context.Request.MatchedParameters["deployment-id"] == "gpt-4o-mini")">
+            <llm-token-limit
+                counter-key="@("gpt4omini-" + context.Subscription.Id)"
+                tokens-per-minute="20000"
+                estimate-prompt-tokens="true" />
+        </when>
+    </choose>
+</inbound>
+```
+
+### Token Quota (Periodisk)
+
+Sett token-kvoter per dag, uke eller måned:
+
+```xml
+<inbound>
+    <base />
+    <!-- Daglig token-kvote per avdeling -->
+    <llm-token-limit
+        counter-key="@(context.Request.Headers.GetValueOrDefault("X-Department", "default"))"
+        tokens-per-minute="0"
+        token-quota="100000"
+        token-quota-period="86400"
+        estimate-prompt-tokens="true"
+        remaining-tokens-variable-name="dailyRemaining" />
+
+    <!-- Legg til gjenværende kvote i respons-header -->
+    <set-header name="X-Daily-Tokens-Remaining" exists-action="override">
+        <value>@(context.Variables.GetValueOrDefault<int>("dailyRemaining").ToString())</value>
+    </set-header>
+</inbound>
+```
+
+### Prompt Token Pre-calculation
+
+`estimate-prompt-tokens="true"` lar APIM estimere prompt-tokens FØR request sendes til backend. Hvis prompten allerede overskrider grensen, returneres 429 umiddelbart:
+
+```
+Med pre-calculation:
+  Klient → APIM (estimerer: 8000 tokens, grense: 5000) → 429 returnert
+  → Ingen request til Azure OpenAI → sparer backend-kapasitet
+
+Uten pre-calculation:
+  Klient → APIM → Azure OpenAI (bruker 8000 tokens) → Respons → APIM teller → Neste request: 429
+  → Tokens allerede brukt
+```
+
+### Multi-Region Rate Limiting
+
+**Viktig:** Rate limiting-policyer (`llm-token-limit`, `rate-limit`) teller SEPARAT per regional gateway i multi-region deployments:
+
+| Policy | Scope | Multi-region oppførsel |
+|--------|-------|----------------------|
+| `llm-token-limit` | Per gateway | Separate tellere per region |
+| `rate-limit` | Per gateway | Separate tellere per region |
+| `quota` | Global (instans) | Én global teller |
+| `quota-by-key` | Global (instans) | Én global teller |
+
+For å oppnå global rate limiting, bruk `quota-by-key` i stedet for `llm-token-limit`.
+
+---
+
+## Audit Logging for Prompts
+
+### Aktivere LLM API-logging
+
+```
+1. APIM → Monitoring → Diagnostic settings
+2. "+ Add diagnostic setting"
+3. Velg "Logs related to generative AI gateway"
+4. Destination: Log Analytics workspace
+5. Save
+
+6. APIM → APIs → [din API] → Settings → Diagnostic Logs
+7. Azure Monitor → Log LLM messages: Enabled
+8. Log prompts: 32768 bytes
+9. Log completions: 32768 bytes
+10. Save
+```
+
+### Log-skjema: ApiManagementGatewayLlmLog
+
+| Felt | Beskrivelse | Eksempel |
+|------|-------------|---------|
+| `TimeGenerated` | Tidspunkt for request | 2026-02-11T10:30:00Z |
+| `CorrelationId` | Unik request-ID | abc-123-def |
+| `OperationName` | API-operasjon | ChatCompletions |
+| `ModelDeployment` | Deployment-navn | gpt-4o |
+| `PromptTokens` | Antall prompt-tokens | 150 |
+| `CompletionTokens` | Antall completion-tokens | 250 |
+| `TotalTokens` | Totalt token-forbruk | 400 |
+| `RequestMessages` | Prompt-innhold (JSON) | [{"role":"user","content":"..."}] |
+| `ResponseMessages` | Completion-innhold (JSON) | [{"content":"..."}] |
+
+### KQL: Audit Trail for AI-requests
+
+```kusto
+// Full audit trail med prompt og respons
+ApiManagementGatewayLlmLog
+| where TimeGenerated > ago(24h)
+| extend RequestArray = parse_json(RequestMessages)
+| extend ResponseArray = parse_json(ResponseMessages)
+| mv-expand RequestArray
+| mv-expand ResponseArray
+| project
+    TimeGenerated,
+    CorrelationId,
+    Model = ModelDeployment,
+    PromptTokens,
+    CompletionTokens,
+    Prompt = tostring(RequestArray.content),
+    Response = tostring(ResponseArray.content)
+| summarize
+    Input = strcat_array(make_list(Prompt), " "),
+    Output = strcat_array(make_list(Response), " ")
+  by CorrelationId, TimeGenerated, Model, PromptTokens, CompletionTokens
+| where isnotempty(Input) and isnotempty(Output)
+| order by TimeGenerated desc
+```
+
+### KQL: Detektere Anomalier
+
+```kusto
+// Finn uvanlig høy token-bruk per bruker
+ApiManagementGatewayLlmLog
+| where TimeGenerated > ago(7d)
+| extend UserId = tostring(CustomDimensions["UserId"])
+| summarize
+    AvgTokens = avg(toint(TotalTokens)),
+    MaxTokens = max(toint(TotalTokens)),
+    P95Tokens = percentile(toint(TotalTokens), 95),
+    RequestCount = count()
+  by UserId, bin(TimeGenerated, 1h)
+| where MaxTokens > 3 * AvgTokens  // Flagg anomalier
+| order by MaxTokens desc
+```
+
+### Event Hub-logging for Real-time Monitoring
+
+```xml
+<outbound>
+    <base />
+    <!-- Logg til Event Hub for real-time analyse -->
+    <log-to-eventhub logger-id="ai-audit-logger">@{
+        var body = context.Response.Body.As<JObject>(preserveContent: true);
+        var usage = body?["usage"];
+
+        return new JObject(
+            new JProperty("timestamp", DateTime.UtcNow.ToString("o")),
+            new JProperty("correlationId", context.RequestId),
+            new JProperty("subscriptionId", context.Subscription?.Id),
+            new JProperty("apiId", context.Api?.Id),
+            new JProperty("model", body?["model"]?.ToString()),
+            new JProperty("promptTokens", usage?["prompt_tokens"]),
+            new JProperty("completionTokens", usage?["completion_tokens"]),
+            new JProperty("totalTokens", usage?["total_tokens"]),
+            new JProperty("statusCode", context.Response.StatusCode),
+            new JProperty("region", context.Deployment.Region),
+            new JProperty("latencyMs", context.Elapsed.TotalMilliseconds)
+        ).ToString();
+    }</log-to-eventhub>
+</outbound>
+```
+
+---
+
+## Komplett GenAI Policy Stack
+
+### Full Inbound + Outbound Policy
+
+```xml
+<policies>
+    <inbound>
+        <base />
+
+        <!-- 1. Autentisering -->
+        <validate-azure-ad-token tenant-id="{{TENANT_ID}}"
+                                 header-name="Authorization"
+                                 failed-validation-httpcode="401" />
+
+        <!-- 2. Ekstraher brukerinfo for logging og rate limiting -->
+        <set-variable name="caller-id"
+                      value="@(context.Request.Headers.GetValueOrDefault("Authorization","")
+                             .AsJwt()?.Claims.GetValueOrDefault("oid", "anonymous"))" />
+        <set-variable name="department"
+                      value="@(context.Request.Headers.GetValueOrDefault("Authorization","")
+                             .AsJwt()?.Claims.GetValueOrDefault("department", "unknown"))" />
+
+        <!-- 3. Token rate limiting -->
+        <llm-token-limit
+            counter-key="@((string)context.Variables["caller-id"])"
+            tokens-per-minute="10000"
+            estimate-prompt-tokens="true" />
+
+        <!-- 4. Content Safety -->
+        <llm-content-safety backend-id="content-safety-backend"
+                            shield-prompt="true">
+            <categories output-type="EightSeverityLevels">
+                <category name="Hate" threshold="4" />
+                <category name="Violence" threshold="4" />
+                <category name="SelfHarm" threshold="2" />
+                <category name="Sexual" threshold="2" />
+            </categories>
+        </llm-content-safety>
+
+        <!-- 5. Semantic cache lookup -->
+        <llm-semantic-cache-lookup
+            score-threshold="0.9"
+            embeddings-backend-id="embedding-backend"
+            embeddings-backend-auth="system-assigned" />
+
+        <!-- 6. Backend med managed identity -->
+        <set-backend-service backend-id="aoai-pool" />
+        <authentication-managed-identity
+            resource="https://cognitiveservices.azure.com"
+            output-token-variable-name="mi-token" />
+        <set-header name="Authorization" exists-action="override">
+            <value>@("Bearer " + (string)context.Variables["mi-token"])</value>
+        </set-header>
+    </inbound>
+
+    <backend>
+        <forward-request timeout="120"
+                         fail-on-error-status-code="true"
+                         buffer-response="false" />
+    </backend>
+
+    <outbound>
+        <base />
+
+        <!-- 7. Semantic cache store -->
+        <llm-semantic-cache-store duration="3600" />
+
+        <!-- 8. Token-metriker -->
+        <llm-emit-token-metric namespace="ai-metrics">
+            <dimension name="UserId" value="@((string)context.Variables["caller-id"])" />
+            <dimension name="Department" value="@((string)context.Variables["department"])" />
+            <dimension name="API" value="@(context.Api.Name)" />
+            <dimension name="Region" value="@(context.Deployment.Region)" />
+        </llm-emit-token-metric>
+    </outbound>
+
+    <on-error>
+        <base />
+        <return-response>
+            <set-status code="500" reason="Internal Server Error" />
+            <set-body>{
+    "error": {
+        "code": "GatewayError",
+        "message": "An error occurred processing your AI request."
+    }
+}</set-body>
+        </return-response>
+    </on-error>
+</policies>
+```
+
+### Policy Execution Order
+
+```
+Inbound (fra topp til bunn):
+  1. Authentication (validate-azure-ad-token)
+  2. Variable extraction (set-variable)
+  3. Token rate limiting (llm-token-limit)
+  4. Content Safety (llm-content-safety)
+  5. Cache lookup (llm-semantic-cache-lookup)
+  6. Backend selection (set-backend-service)
+  7. Backend auth (authentication-managed-identity)
+
+Backend:
+  8. Forward request (forward-request)
+
+Outbound (fra topp til bunn):
+  9. Cache store (llm-semantic-cache-store)
+  10. Emit metrics (llm-emit-token-metric)
+```
+
+---
+
+## GenAI Policy Referanse
+
+### Alle GenAI-spesifikke Policyer
+
+| Policy | Fase | Formål |
+|--------|------|--------|
+| `llm-content-safety` | Inbound | Content Safety moderering |
+| `llm-token-limit` | Inbound | Token rate limiting |
+| `llm-semantic-cache-lookup` | Inbound | Semantic cache oppslag |
+| `llm-semantic-cache-store` | Outbound | Lagre i semantic cache |
+| `llm-emit-token-metric` | Outbound | Emitter token-metriker |
+
+### Kompatibilitet
+
+| Policy | Classic | V2 | Consumption | Self-hosted | Workspace |
+|--------|---------|-----|-------------|-------------|-----------|
+| `llm-content-safety` | Ja | Ja | Ja | Ja | Ja |
+| `llm-token-limit` | Ja | Ja | Ja | Ja | Ja |
+| `llm-semantic-cache-lookup` | Ja | Ja | Nei | Nei | Ja |
+| `llm-semantic-cache-store` | Ja | Ja | Nei | Nei | Ja |
+| `llm-emit-token-metric` | Ja | Ja | Ja | Ja | Ja |
+
+---
+
+## Referanser
+
+- [AI gateway in Azure API Management](https://learn.microsoft.com/en-us/azure/api-management/genai-gateway-capabilities) — Fullstendig oversikt over AI gateway-kapabiliteter
+- [Enforce content safety checks on LLM requests](https://learn.microsoft.com/en-us/azure/api-management/llm-content-safety-policy) — llm-content-safety policy referanse
+- [LLM token limit policy](https://learn.microsoft.com/en-us/azure/api-management/llm-token-limit-policy) — llm-token-limit policy referanse
+- [llm-emit-token-metric policy](https://learn.microsoft.com/en-us/azure/api-management/llm-emit-token-metric-policy) — Token-metrikk policy referanse
+- [llm-semantic-cache-lookup policy](https://learn.microsoft.com/en-us/azure/api-management/llm-semantic-cache-lookup-policy) — Semantic cache lookup referanse
+- [llm-semantic-cache-store policy](https://learn.microsoft.com/en-us/azure/api-management/llm-semantic-cache-store-policy) — Semantic cache store referanse
+- [Prompt Shields - Azure AI Content Safety](https://learn.microsoft.com/en-us/azure/ai-services/content-safety/concepts/jailbreak-detection) — Prompt Shield-dokumentasjon
+- [Log token usage, prompts, and completions](https://learn.microsoft.com/en-us/azure/api-management/api-management-howto-llm-logs) — LLM-logging i APIM
+
+---
+
+## For Cosmo
+
+- **Bruk denne referansen** når kunder trenger å implementere AI safety og governance gjennom APIM-policyer, spesielt content safety, prompt-validering og audit-logging.
+- Den viktigste policyen for norsk offentlig sektor er `llm-content-safety` med `shield-prompt="true"` — dette blokkerer jailbreak-forsøk og uønsket innhold FØR det når modellen.
+- Husk rekkefølgen: Autentisering FØRST, deretter rate limiting, SÅ content safety, SÅ cache lookup. Content Safety koster tokens (kall til Content Safety API) — cache lookup etter content safety betyr at cachen kun inneholder "godkjent" innhold.
+- For audit-logging: Aktiver LLM API-logging i Diagnostic Settings. Dette gir full etterprøvbarhet for alle prompts og completions — noe som er påkrevd under AI Act for høy-risiko AI-systemer.
+- Rate limiting per modell er viktig: GPT-4o er dyrere enn GPT-4o-mini, og bør ha strengere token-grenser for å kontrollere kostnader.