NeuralTrust | Platform for Agent Security.

TrustLens supports all four Azure AI agent infrastructure models under a single integration:

Azure AI Foundry Agents (v2) — agents built with the Azure AI Foundry Agents SDK, hosted in AI Services accounts (kind=AIServices)
AI Hub projects — enterprise AI Foundry setups created before late 2024, backed by Microsoft.MachineLearningServices/workspaces with kind=Project
Azure OpenAI Classic Assistants (v1) — assistants built with the Azure OpenAI Assistants API (kind=OpenAI)
Legacy ML Workspaces — pre-AI-Foundry Azure ML Studio workspaces (kind=Default) that can host Promptflow-based agents

A single integration discovers all four types automatically from your subscription.

How to identify your infrastructure model: In the Azure Portal, navigate to your AI Foundry project. If the URL shows ai.azure.com and the resource is under a Microsoft.CognitiveServices account, you are on the AI Services model (v2). If your project lives under a Microsoft.MachineLearningServices Hub, you are on the AI Hub model.You can also check from the CLI: az resource list --resource-type Microsoft.MachineLearningServices/workspaces --query "[].{name:name, kind:kind}"

What TrustLens discovers

Azure AI Foundry agents (v2)

Data	Source
Agent name, description, status	Azure AI Foundry Agents API
Model, instructions, tools	Azure AI Foundry Agents API
Knowledge bases (vector stores)	Azure AI Foundry Agents API
Content filters / RAI policies	Cognitive Services Management API
Guardrails configuration	Derived from RAI policies
Usage metrics (runs, tokens, latency, errors, tool calls)	Application Insights via Log Analytics (requires telemetry setup — see below)

AI Hub projects (ML Workspace-backed)

Data	Source
Agent name, description, status	Azure AI Foundry Agents API (via ML Workspace endpoint)
Model, instructions, tools	Azure AI Foundry Agents API
Knowledge bases (vector stores)	Azure AI Foundry Agents API
Model deployments	Azure AI Projects API

RAI policies and content filters are not available for AI Hub projects. These fields will show as unavailable for hub-backed agents.

Azure OpenAI Classic assistants (v1)

Data	Source
Assistant name, description, status	Azure OpenAI Assistants API
Model, instructions, tools	Azure OpenAI Assistants API
Vector stores (knowledge bases)	Azure OpenAI Assistants API
Usage metrics (runs, conversations, tool call counts)	Assistants API — Threads & Run Steps (no Log Analytics required)

Content filters and guardrails are not exposed via the Azure OpenAI Assistants (v1) API. These fields will show as unavailable for Classic assistants.

Legacy ML Workspaces

Data	Source
Agent name, description, status	Azure AI Foundry Agents API (via ML Workspace endpoint)
Model deployments	Azure AI Projects API

Legacy ML Workspaces may not expose the full Agents API surface. TrustLens will discover what is available and skip unsupported endpoints gracefully.

Required permissions

The required roles depend on which infrastructure model your agents use. If you are unsure, assign all applicable roles — unused roles do not cause errors.

Azure AI Foundry (v2) — AI Services model

Simple setup — one role (recommended)

Assign Azure AI User to the service principal at the subscription level:

Role	Scope	Purpose
`Azure AI User`	Subscription	Discover Cognitive Services accounts and projects (management plane), list agents, models, datasets, and vector stores (data plane), read RAI policies and content filters

Why this role: Azure AI User includes both Microsoft.CognitiveServices/*/read (management plane — enumerate accounts, read RAI policies, read diagnostic settings) and Microsoft.CognitiveServices/* (data plane — access agents, models, datasets). Assigning it at subscription level means it applies to all AI Services resources in the subscription automatically.

Granular setup — two roles (least-privilege alternative)

If your security policy requires strictly minimal permissions, you can use two more targeted roles instead:

Role	Scope	Purpose
`Reader`	Subscription	Enumerate Cognitive Services accounts, read account metadata, read RAI policies, read diagnostic settings
`Cognitive Services OpenAI User`	Each AI Services / OpenAI resource	Access the data plane to list agents, models, assistants, vector stores

With the granular approach, you must assign Cognitive Services OpenAI User on each AI Services resource individually. Using Azure AI User at subscription level is simpler and equally secure for read-only access.

AI Hub projects — ML Workspace-backed model

If your agents were created via an AI Foundry Hub (enterprise setup before late 2024), they live under Microsoft.MachineLearningServices/workspaces resources. The roles required are different from the AI Services model:

Role	Scope	Purpose
`Reader`	Subscription	Enumerate ML Workspace resources
`Azure AI Developer`	Each AI Hub / Project workspace	Access the Agents API data plane on Hub-backed projects

Azure AI Developer grants access to Microsoft.MachineLearningServices/workspaces/*/read and the data plane actions needed to list agents. Assign it at the Hub or Project workspace scope, or at the subscription level if you have multiple hubs.

Azure AI Developer does not include Microsoft.CognitiveServices/*/read. If you have a mix of AI Services (v2) and AI Hub projects, you need both Azure AI User (for AI Services) and Azure AI Developer (for AI Hub). Reader at subscription level covers resource enumeration for both.

Legacy ML Workspaces — pre-AI-Foundry Azure ML Studio

For older workspaces created before AI Foundry existed (Azure ML Studio / kind=Default):

Role	Scope	Purpose
`Reader`	Subscription	Enumerate ML Workspace resources
`Azure Machine Learning Data Scientist`	Each ML Workspace	Access the Agents API data plane on legacy workspaces

All infrastructure types — single subscription setup (recommended)

If you have a mix of infrastructure models, or simply want a zero-friction setup that covers everything without tracking which roles apply to which resources, assign all roles at subscription level. Azure RBAC cascades automatically to all child resources.

Core roles (always required)

Role	Scope	Covers
`Reader`	Subscription	Control-plane enumeration of all resource types: Cognitive Services accounts, ML Workspaces, App Insights components, diagnostic settings, resource groups
`Azure AI User`	Subscription	Data plane for AI Services (v2) and Azure OpenAI Classic (v1): list agents, models, vector stores, RAI policies

Conditional roles (add only what applies to you)

Role	Scope	Required for
`Azure AI Developer`	Subscription	Data plane for AI Hub / ML Workspace-backed projects (pre-late-2024 Foundry Hubs)
`Azure Machine Learning Data Scientist`	Subscription	Data plane for legacy ML Workspaces (`kind=Default`, pre-AI-Foundry Azure ML Studio)
`Log Analytics Reader`	Subscription	v2 usage telemetry via App Insights / Log Analytics
`Monitoring Reader`	Subscription	Hub-backed agent metrics via Azure Monitor (`AgentRuns`, `AgentTokens`, etc.)

Reader and Azure AI User are always required. Neither can replace the other: Reader handles broad ARM control-plane enumeration (ML Workspaces, App Insights, etc.) while Azure AI User adds the Cognitive Services data plane. Azure AI User does not include Microsoft.MachineLearningServices or Microsoft.Insights read permissions.

One-command setup

SUBSCRIPTION_ID=$(az account show --query id -o tsv)
APP_ID="<your-app-id>"
SCOPE="/subscriptions/$SUBSCRIPTION_ID"

# Always required
az role assignment create --assignee $APP_ID --role "Reader"         --scope $SCOPE
az role assignment create --assignee $APP_ID --role "Azure AI User"  --scope $SCOPE

# Add if you have AI Hub / ML Workspace-backed projects
az role assignment create --assignee $APP_ID --role "Azure AI Developer" --scope $SCOPE

# Add if you have legacy ML Workspaces (kind=Default)
az role assignment create --assignee $APP_ID --role "Azure Machine Learning Data Scientist" --scope $SCOPE

# Add for usage telemetry (v2 App Insights + Hub metrics)
az role assignment create --assignee $APP_ID --role "Log Analytics Reader" --scope $SCOPE
az role assignment create --assignee $APP_ID --role "Monitoring Reader"    --scope $SCOPE

Optional — telemetry (usage metrics)

TrustLens collects two distinct types of telemetry depending on your infrastructure model:

AI Foundry v2 (AI Services): Usage metrics, token counts, latency, error rates, and tool call breakdowns are collected from Application Insights via a linked Log Analytics workspace.
AI Hub (ML Workspace-backed): Agent-level metrics (AgentRuns, AgentTokens, AgentThreads, AgentToolCalls, AgentMessages) are collected from Azure Monitor Metrics on the Hub resource.

Role	Scope	Purpose
`Log Analytics Reader`	Log Analytics Workspace (or Subscription)	Query Application Insights traces (`AppDependencies` table) for v2 usage metrics, token counts, latency, and tool call breakdowns
`Monitoring Reader`	AI Foundry Hub resource (or Subscription)	Query Azure Monitor Metrics (`AgentRuns`, `AgentTokens`, `AgentThreads`, etc.) for Hub-backed agent telemetry

Both roles can be assigned at subscription level to cascade automatically to all workspaces and resources in the subscription, eliminating the need for per-resource assignments.

What you lose without these: Usage statistics and tool call metrics will be unavailable for the corresponding infrastructure type. v1 Classic usage is collected directly via the Assistants API and does not require either role. What you gain: Per-agent usage metrics, conversation-level telemetry signals (used for aggregation, not enumeration), per-tool call breakdown by type (code interpreter, file search, custom functions), token consumption, latency, and Hub-level run and thread counts.

Prerequisites

Before configuring the integration, ensure you have:

An Azure subscription containing AI Services, Azure OpenAI, ML Workspace, or AI Hub resources
Permission to create app registrations and assign RBAC roles (User Access Administrator or Owner on the subscription)
For v2 telemetry: an Application Insights resource connected to your AI Foundry project, linked to a Log Analytics workspace (see Enabling telemetry)

Step-by-step setup

Create a service principal

Azure CLI
Azure Portal

az ad sp create-for-rbac --name "neuraltrust-trustlens" --skip-assignment

Save the output — you will need appId (client ID), password (client secret), and tenant (tenant ID).

Go to Microsoft Entra ID → App registrations → New registration
Name the app (e.g., neuraltrust-trustlens)
Select Single tenant
Click Register
Go to Certificates & secrets → New client secret, set an expiry, and copy the secret value immediately

Assign the Azure AI User role

Azure CLI
Azure Portal

SUBSCRIPTION_ID=$(az account show --query id -o tsv)
APP_ID="<your-app-id>"

az role assignment create \
  --assignee $APP_ID \
  --role "Azure AI User" \
  --scope /subscriptions/$SUBSCRIPTION_ID

(Optional) Assign telemetry roles

Skip this step if you do not need usage metrics. Two roles cover different telemetry sources:

Log Analytics Reader — required for v2 (AI Services) usage metrics via Application Insights
Monitoring Reader — required for Hub-backed agent metrics via Azure Monitor

Azure CLI — subscription scope (simplest)
Azure CLI — resource scope (least privilege)
Azure Portal

Assigning at subscription level cascades to all workspaces and Hub resources automatically:

SUBSCRIPTION_ID=$(az account show --query id -o tsv)

az role assignment create \
  --assignee $APP_ID \
  --role "Log Analytics Reader" \
  --scope /subscriptions/$SUBSCRIPTION_ID

az role assignment create \
  --assignee $APP_ID \
  --role "Monitoring Reader" \
  --scope /subscriptions/$SUBSCRIPTION_ID

Assign Log Analytics Reader on the specific Log Analytics workspace linked to Application Insights:

WORKSPACE_ID=$(az monitor log-analytics workspace show \
  --resource-group <resource-group> \
  --workspace-name <workspace-name> \
  --query id -o tsv)

az role assignment create \
  --assignee $APP_ID \
  --role "Log Analytics Reader" \
  --scope $WORKSPACE_ID

Assign Monitoring Reader on the specific AI Foundry Hub resource:

HUB_ID=$(az resource show \
  --resource-group <resource-group> \
  --resource-type Microsoft.MachineLearningServices/workspaces \
  --name <hub-name> \
  --query id -o tsv)

az role assignment create \
  --assignee $APP_ID \
  --role "Monitoring Reader" \
  --scope $HUB_ID

Configure the integration in TrustLens

Provide the following credentials when creating the Azure integration:

Field	Where to find it
Tenant ID	Azure Portal → Microsoft Entra ID → Overview → Directory (tenant) ID
Client ID	Azure Portal → App registrations → your app → Application (client) ID
Client Secret	Copied in Step 1
Subscription ID	Azure Portal → Subscriptions → your subscription → Subscription ID

Enabling telemetry

TrustLens retrieves v2 usage metrics from Application Insights via a linked Log Analytics workspace. Azure AI Foundry automatically writes server-side OpenTelemetry traces (gen_ai.* semantic conventions) to the AppDependencies table when a Foundry project is connected to Application Insights. TrustLens queries this table to produce per-agent usage, token counts, latency, and tool call breakdowns.

Classic (v1) assistants do not require this setup. Usage for v1 assistants is collected directly from the Assistants API (Threads and Run Steps) and is always available once the core RBAC role is in place.

Connect Application Insights to your AI Foundry project

AI Foundry Portal
Azure CLI

Open Azure AI Foundry and navigate to your project
Go to Settings → Tracing
Select or create an Application Insights resource
Save — Azure will begin writing traces automatically; no SDK instrumentation is required

az ml workspace update \
  --name <foundry-project-name> \
  --resource-group <resource-group> \
  --application-insights /subscriptions/<subscription-id>/resourceGroups/<resource-group>/providers/microsoft.insights/components/<appinsights-name>

Grant Log Analytics Reader on the linked workspace

Application Insights stores trace data in a linked Log Analytics workspace. Find the workspace and grant access:

Azure CLI
Azure Portal

WORKSPACE_ID=$(az monitor log-analytics workspace show \
  --resource-group <resource-group> \
  --workspace-name <workspace-name> \
  --query id -o tsv)

az role assignment create \
  --assignee $APP_ID \
  --role "Log Analytics Reader" \
  --scope $WORKSPACE_ID

Traces may take a few minutes to appear after the first agent invocations.

Telemetry source summary

Data type	Infrastructure	Source	Requires
Usage metrics (runs, tokens, latency, errors)	AI Foundry v2 (AI Services)	Application Insights — `AppDependencies`	App Insights connected to Foundry project + `Log Analytics Reader`
Tool call breakdown (code interpreter, file search, functions)	AI Foundry v2 (AI Services)	Application Insights — `AppDependencies`	Same as above
Agent metrics (runs, tokens, threads, messages, tool calls)	AI Hub (ML Workspace-backed)	Azure Monitor Metrics	`Monitoring Reader` on Hub resource (or subscription)
Usage metrics (runs, conversations, tool call counts)	Azure OpenAI Classic (v1)	Assistants API — Threads & Run Steps	Core RBAC role only — no Log Analytics or Monitor roles needed

Feature availability by permission level

Azure AI Foundry (v2) — AI Services model

Feature	Azure AI User only	+ Log Analytics Reader
Agent discovery	Yes	Yes
Model and dataset discovery	Yes	Yes
Security posture assessment	Yes	Yes
Tools, instructions, knowledge bases	Yes	Yes
Content filters / RAI policies	Yes	Yes
Usage metrics (runs, tokens, latency, errors)	No	Yes
Tool call breakdown (code interpreter, file search, functions)	No	Yes
Conversation-level telemetry (aggregation signals)	No	Yes

AI Hub — ML Workspace-backed model

Feature	Reader + Azure AI Developer	+ Monitoring Reader
Agent discovery	Yes	Yes
Model deployments	Yes	Yes
Tools, instructions, knowledge bases	Yes	Yes
Content filters / RAI policies	No — not available for Hub-backed agents	No — not available for Hub-backed agents
Agent run and thread counts	No	Yes
Token consumption (input / output)	No	Yes
Tool call counts	No	Yes
Message counts	No	Yes

Azure OpenAI Classic (v1)

Feature	Azure AI User only
Assistant discovery	Yes
Tools, instructions, vector stores	Yes
Usage metrics (runs, conversations, tool call counts)	Yes
Content filters / RAI policies	No — Azure API limitation

Known limitations

Limitation	Details
No conversation enumeration for v2	The Azure AI Foundry API does not expose a `conversations.list()` endpoint. App Insights provides conversation-level telemetry signals (conversation ID, message count, latency per conversation) used for aggregation — not a list of conversations you can browse.
Tool call telemetry requires App Insights (v2)	Tool call breakdown (code interpreter, file search, function calls) requires Application Insights connected to the Foundry project. Foundry automatically emits `gen_ai.tool.name` spans — no client instrumentation needed.
Content filters unavailable for v1	The Azure OpenAI Assistants (v1) API does not expose content filter or RAI policy configuration. These fields show as unavailable for Classic assistants.
App Insights trace delay	Traces may take a few minutes to appear in Log Analytics after initial agent invocations.
Conversation ID mismatch (v2)	Application Insights `conversation_id` values (UUID format) do not match Foundry API conversation IDs (`conv_xxx` format). TrustLens uses telemetry-level aggregation.

Security considerations

The service principal has read-only access. TrustLens cannot modify, delete, or create any Azure resources.
Azure AI User at subscription level includes listkeys/action (the ability to read API keys for Cognitive Services accounts). If your policy prohibits this, use the granular setup (Reader + Cognitive Services OpenAI User), which does not include key listing.
Client secrets should be rotated regularly. Update the integration in TrustLens when you rotate the secret.
All credentials are encrypted at rest.

Troubleshooting

No agents found in my subscription

Verify the service principal has Azure AI User at the subscription level (not at a resource group or resource level only).
Confirm your AI agents are deployed in AI Services accounts with kind=AIServices or kind=OpenAI. If they are in a different resource type, contact support.
Check that the Subscription ID entered in the integration matches the subscription where your agents are deployed.

Usage statistics show as zero or unavailable (v2)

Verify that Application Insights is connected to your AI Foundry project (Foundry Portal → Settings → Tracing).
Verify the service principal has Log Analytics Reader on the Log Analytics workspace linked to Application Insights (found under App Insights → Settings → Linked workspace).
Traces appear within minutes of the first agent invocation. If no invocations have occurred, usage metrics will correctly show zero.

Usage statistics show as zero or unavailable (AI Hub / ML Workspace-backed)

Hub agent metrics are collected from Azure Monitor Metrics, not Application Insights.
Verify the service principal has Monitoring Reader on the AI Foundry Hub resource (or at subscription level).
If no agent runs have occurred, metrics will correctly show zero.

Usage statistics show as zero or unavailable (v1 Classic)

Classic usage is collected via the Assistants API (Threads and Run Steps) — no Log Analytics configuration is needed.
Verify the service principal has data plane access (Azure AI User or Cognitive Services OpenAI User) on the Azure OpenAI resource.
If no threads or runs exist for an assistant, usage will correctly show zero.

Content filters showing as unavailable for some agents

Content filter data is only available for Azure AI Foundry (v2) agents. Classic (v1) assistants do not expose this via API — this is an Azure limitation, not a configuration issue.

403 Forbidden errors during sync

The service principal may not have the role assigned at the correct scope. Verify the role assignment is at subscription level, not resource group or resource level only.
Role assignments can take a few minutes to propagate after being created.

Classic assistants not being discovered

Classic assistants require the service principal to have data plane access to the Azure OpenAI resource. With Azure AI User at subscription level this is covered automatically. With the granular setup, verify that Cognitive Services OpenAI User is assigned on the specific OpenAI resource.

Introduction

Concepts

Integrations

Reference

Azure

What TrustLens discovers

Azure AI Foundry agents (v2)

AI Hub projects (ML Workspace-backed)

Azure OpenAI Classic assistants (v1)

Legacy ML Workspaces

Required permissions

Azure AI Foundry (v2) — AI Services model

Simple setup — one role (recommended)

Granular setup — two roles (least-privilege alternative)

AI Hub projects — ML Workspace-backed model

Legacy ML Workspaces — pre-AI-Foundry Azure ML Studio

All infrastructure types — single subscription setup (recommended)

Core roles (always required)

Conditional roles (add only what applies to you)

One-command setup

Optional — telemetry (usage metrics)

Prerequisites

Step-by-step setup

Enabling telemetry

Telemetry source summary

Feature availability by permission level

Azure AI Foundry (v2) — AI Services model

AI Hub — ML Workspace-backed model

Azure OpenAI Classic (v1)

Known limitations

Security considerations

Troubleshooting

​What TrustLens discovers

​Azure AI Foundry agents (v2)

​AI Hub projects (ML Workspace-backed)

​Azure OpenAI Classic assistants (v1)

​Legacy ML Workspaces

​Required permissions

​Azure AI Foundry (v2) — AI Services model

​Simple setup — one role (recommended)

​Granular setup — two roles (least-privilege alternative)

​AI Hub projects — ML Workspace-backed model

​Legacy ML Workspaces — pre-AI-Foundry Azure ML Studio

​All infrastructure types — single subscription setup (recommended)

​Core roles (always required)

​Conditional roles (add only what applies to you)

​One-command setup

​Optional — telemetry (usage metrics)

​Prerequisites

​Step-by-step setup

​Enabling telemetry

​Telemetry source summary

​Feature availability by permission level

​Azure AI Foundry (v2) — AI Services model

​AI Hub — ML Workspace-backed model

​Azure OpenAI Classic (v1)

​Known limitations

​Security considerations

​Troubleshooting

What TrustLens discovers

Azure AI Foundry agents (v2)

AI Hub projects (ML Workspace-backed)

Azure OpenAI Classic assistants (v1)

Legacy ML Workspaces

Required permissions

Azure AI Foundry (v2) — AI Services model

Simple setup — one role (recommended)

Granular setup — two roles (least-privilege alternative)

AI Hub projects — ML Workspace-backed model

Legacy ML Workspaces — pre-AI-Foundry Azure ML Studio

All infrastructure types — single subscription setup (recommended)

Core roles (always required)

Conditional roles (add only what applies to you)

One-command setup

Optional — telemetry (usage metrics)

Prerequisites

Step-by-step setup

Enabling telemetry

Telemetry source summary

Feature availability by permission level

Azure AI Foundry (v2) — AI Services model

AI Hub — ML Workspace-backed model

Azure OpenAI Classic (v1)

Known limitations

Security considerations

Troubleshooting