Connectors · LangDock Private Preview

LangDock

Rencore monitors LangDock across 14 governance policies, 9 reports, and 11 inventories, detecting cost overruns, access issues, and workspace sprawl automatically.

AI & Agents

Published 16 May 2026 For Head of IT, CISO

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Rencore LangDock governance is a set of 14 policies, 9 reports, 9 segments, and 11 inventories that audit LangDock's AI platform for cost overruns, access control gaps, and workspace sprawl. It detects users exceeding cost limits, workspaces without assigned owners, inactive accounts consuming licenses, and usage patterns indicating potential data governance risks.

47 governance capabilities: 11 inventories · 14 policies · 9 reports · 9 segments · 1 automations

Why govern LangDock with Rencore

Control AI platform costs

Track spending at the workspace and user level. Policies alert when costs exceed thresholds. Reports break down spending by model, workspace, and user for budget accountability.
Manage user access

Detect users deactivated in Entra ID who still have LangDock access, external users without business justification, and accounts with excessive permissions.
Govern workspaces

Find inactive workspaces without recent activity, workspaces without assigned owners, and usage patterns that indicate potential data governance concerns. Reports show workspace activity trends.

What Rencore discovers

Rencore automatically inventories these LangDock object types.

LangDock Workspace

A LangDock workspace representing an organization
LangDock User

A user in the LangDock workspace with usage metrics for a specific time period
LangDock Agent

An AI agent/assistant in the LangDock workspace with usage metrics
LangDock Project

A project in the LangDock workspace with usage metrics
LangDock Model Usage

Usage metrics for an AI model in the LangDock workspace
LangDock Audit Log

Audit log entries from the LangDock workspace
LangDock User Agent Usage

Usage record linking a LangDock user to an agent with message count for a specific period
LangDock User Model Usage

Usage record linking a LangDock user to an AI model with message count for a specific period
LangDock User Project Usage

Usage record linking a LangDock user to a project with message count for a specific period
LangDock Integration

A third-party integration configured in the LangDock workspace (e.g., SharePoint, Jira, Slack)
LangDock Integration Action

An action available within a LangDock integration (e.g., 'Search documents' in SharePoint)

How LangDock governance works in Rencore

Rencore connects to LangDock via the LangDock API and inventories workspaces, users, assistants, usage data, and cost data. Policies run on every scan cycle and flag cost overruns, access issues, and workspace sprawl with severity levels and recommended actions.

Who uses LangDock governance

Heads of IT use cost policies to keep AI platform spending within approved budgets. CISOs review user access policies to enforce identity governance. Teams evaluating LangDock alongside other AI platforms use the reports to compare usage and cost patterns.

Getting started

Provide Rencore with LangDock API credentials. All 14 policies activate on first scan, covering workspaces, users, and cost data automatically.

Policies

14 governance rules that detect violations and risks.

Severity Category

LangDock User has admin role

Detects LangDock users who have the admin role assigned.

Medium Security
LangDock Agent has high message volume

Detects LangDock agents that received more than 200 messages in the period.

Medium Operation
LangDock Model has high request count

Detects models with more than 1000 requests in the reporting period.

Medium Costs
LangDock User is deactivated in Entra ID

Detects LangDock users whose linked Microsoft 365 account is disabled.

Medium Security
LangDock User is external user in Entra ID

Detects LangDock users whose linked Microsoft 365 account is a guest/external user.

Medium Security
LangDock Agent is unused

Detects LangDock agents that had no user interactions in the reporting period.

Medium Sprawl
LangDock User has high agent usage

Detects user-agent combinations where the user sent more than 100 messages to a single agent.

Medium Costs
LangDock User has high model consumption

Detects user-model combinations where the user sent more than 500 messages using a single model.

Medium Costs
LangDock Integration uses OAuth

Detects LangDock integrations that authenticate via OAuth, which should be reviewed for access scope.

Medium Security
LangDock Agent has no description

Detects LangDock agents that have an empty or missing description.

Low Operation
LangDock User is inactive

Detects LangDock users with zero messages in the reporting period.

Low Sprawl
LangDock Agent used by single user

Detects LangDock agents that were used by only one user in the reporting period.

Low Operation
LangDock Integration has no actions

Detects LangDock integrations that have zero configured actions.

Low Sprawl
LangDock workspace without projects

Detects LangDock workspaces that contain no projects

Low Sprawl

Need a rule that isn't listed? Rencore's Policy Builder lets you create custom policies tailored to your organization. Learn more about the Policy Builder

Reports

9 analytics views and dashboards.

Messages per LangDock user

Shows the top 10 users by total message count

Bar Chart · Operation
Messages per LangDock agent

Shows the top 10 agents by message count

Bar Chart · Operation
LangDock model usage distribution

Shows the distribution of requests across AI models

Bar Chart · Costs
Agent usage per user

Shows top 10 users by total agent message count

Bar Chart · Operation
Model usage by user

Shows the distribution of messages across AI models per user

Bar Chart · Costs
Agent adoption (unique users)

Shows the number of unique users per agent

Bar Chart · Adoption
Integrations by auth type

Shows the distribution of integrations by authentication type

Donut Chart · Security
Actions per integration

Shows the top integrations by number of available actions

Bar Chart · Operation
Token usage by model

Shows total prompt token consumption per model

Bar Chart · Costs

Automations

1 automated remediation workflows.

Disable LangDock Agent

Automatically disables a LangDock agent after approval

Segments

9 data groupings for targeted filtering.

LangDock Admin Users

Shows LangDock users with admin role
Active LangDock Agents

Shows LangDock agents with messages in the current period
High Usage LangDock Models

Shows models with more than 1000 requests in the period
High Agent Usage

Shows user-agent combinations with more than 50 messages in the period
High Model Usage

Shows user-model combinations with more than 100 messages in the period
OAuth Integrations

Shows integrations using OAuth authentication
API Key Integrations

Shows integrations using API key authentication
Active LangDock Users

Users who have sent at least one message
Inactive LangDock Users

Users who have not sent any messages

Frequently asked questions

Does Rencore support governance for AI tools beyond Microsoft Copilot?

Yes. Rencore connects to Claude, OpenAI, Gemini, GitHub Copilot, Cursor, Windsurf, AWS Bedrock, Azure AI Foundry, and other AI platforms. Each connector provides tailored policies for cost management, security, adoption tracking, and access control, giving IT a unified governance view across all AI tools the organization uses.

What is Rencore governance?

Rencore governance is a SaaS platform that continuously monitors your Microsoft 365 tenant for policy violations, configuration drift, and security risks across SharePoint, Teams, Power Platform, Copilot, and AI Agents. It automates compliance evidence collection, surfaces oversharing and sprawl, and provides actionable remediation workflows, reducing manual audit effort by up to 80%.

How do Rencore policies work?

Rencore ships with hundreds of pre-built policies that detect governance violations across every connector, oversharing, sprawl, cost overruns, security risks, and compliance gaps. Policies run on a continuous schedule, evaluate each discovered object against configurable rules, and flag violations with severity (High, Medium, Low), category, and a recommended action.

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