MCP Governance

Governance for trusted AI action across MCP systems

Run AI that reads, writes and updates across MCP-connected systems with the control, visibility, and boundaries required for enterprise teams.

Enable safe AI adoption at scale

Barndoor sits in the execution path for AI—enforcing policy, routing tools intelligently, and recording every action as AI operates across MCP-connected systems.

The control layer for enterprise AI adoption

Granular access controls

Centralize visibility and control as AI and MCP usage scales across your environment.

• Flexible by design: Work with any AI app, agent framework, and MCP server—local, vendor-hosted, or third-party.
• Machine-to-machine authentication: Authenticate AI apps and agents using secure, non-human credentials.
• Universal endpoint: Connect and manage MCP servers through one consistent endpoint.
• Dynamic client registration: Automatically recognize new users with AI apps you’ve connected.

Action-level controls for AI

Enforce least-privilege AI behavior as agents operate across systems in real time.

• Fine-grained access controls: Define exactly what AI can read, write, or modify by user, role, tool, and data.
• Runtime policy enforcement: Enforce predefined policies at execution time before AI accesses MCP tools or data.
• Portable policies: Author policies in code during development and enforce them in production.

Context-aware routing

Enable reliable, multi-system AI workflows by filtering tools and context per request as MCP usage grows.

• Intelligent tool routing: Route each request to only the policy-approved MCP tools required for the task.
• Improve accuracy: Increase reliability and accuracy by up to 95% by filtering irrelevant context.
• Reduce operational cost: Cut token usage and execution costs by up to 95%

Audit every AI action

Get a centralized view of how AI operates across users, agents, and MCP-connected systems.

• Centralized audit dashboard: View AI usage, actions, and policy outcomes across users, agents, and MCPs in one place.
• Action-level audit trails: See approved, denied, and restricted AI actions with full execution context.
• Clear attribution: Tie every action back to a specific user, role, or AI agent.

Why enterprises choose Barndoor

Barndoor brings security, reliability, and trust to AI actions at enterprise scale.

01

Faster path to production

Apply centralized controls across agents, tools, and MCP systems without manual setup.

02

Least-privilege permissions

Enforce scoped AI actions to prevent over-permissioned agents and shadow access.

03

Reliable AI execution

Improve AI accuracy by 95% by limiting tools and context to what each request needs.

04

Lower operational cost

Reduce token usage and infrastructure cost by up to 95% through context filtering.

05

Flexible MCP deployments

Connect to MCP servers running locally, in your cloud, or via third-party providers.

06

No vendor lock-in

Use the AI apps, models, and agent frameworks you choose.

Operational control at scale

Deploy Barndoor where you need it—with the architecture and controls your organization requires.

APIs for control

Manage AI apps, agents, MCP servers, and policies programmatically—no manual setup.

SDKs for enforcement

Enforce policies directly in AI workflows where actions occur.

Built for CI/CD

Version, automate, and deploy policies consistently from development to production.

Frequently asked questions

• What is MCP, and what does it mean for enterprise AI security?

MCP (Model Context Protocol) is an open standard that lets AI models connect to external tools, data sources, and systems, giving agents the ability to read files, query databases, send messages, and trigger actions across your enterprise stack. An AI agent with MCP access can read Slack channels, query Salesforce records, push to GitHub, or write to internal databases.

Before MCP, connecting an AI model to an external system required custom integration work for every tool. MCP standardizes that connection layer, which is why adoption is accelerating: any MCP-compatible model can connect to any MCP server without bespoke code.

• What is MCP governance, and why do enterprise AI teams need it?

MCP governance is runtime enforcement and oversight for AI agents connecting to external tools and systems through the Model Context Protocol, controlling which servers agents can reach, what actions they can take, and what data they can read or write.

Without governance at the MCP layer, AI agents operate on implicit trust: any model with access to a connected tool can invoke any action that tool exposes. For an enterprise running agentic workflows across Slack, Salesforce, GitHub, or internal APIs, that means an agent can read customer data, trigger writes, or exfiltrate information without any policy check in between.

Barndoor MCP Governance addresses this by putting a governance layer between the AI model and every connected MCP server, enforcing access controls by user and group, logging every tool call for audit, and blocking actions that violate policy before they reach the downstream system. This is distinct from configuration-level controls set once at setup; MCP Governance operates at runtime, on every request.

• How do I control which MCP servers my AI agents can access, and can I set different rules per user, group, or agent?

Yes. Barndoor MCP Governance lets you define granular access policies, so different teams or roles see different sets of available MCP servers and permitted actions, without requiring a company-wide setting for every control.

In practice, this means a sales team can have access to your Salesforce MCP while engineering has access to GitHub, and neither group can invoke the other’s tools without an explicit policy grant.

Permissions are additive across groups: if a user belongs to two groups, any action permitted by either group is allowed through. Write confirmations, the human-in-the-loop prompts that ask an agent to confirm before executing a write operation, can also be toggled at the MCP level, so you can require confirmation for high-stakes systems like Salesforce.

Every policy decision, whether allowed or denied, is recorded in an audit trail. The Barndoor MCP library contains the catalog of verified, governed MCP servers your teams can connect to under these policies.

• How does Barndoor prevent AI agents from accessing or leaking customer data through MCP tool calls?

Barndoor data protection runs on every MCP tool call, inspecting both the request going out and the response coming back, so it can detect and redact sensitive information before it reaches the model or gets written to a downstream system.

With agentic workflows, data exposure often happens through MCP responses. An agent querying a Slack shared channel or a Google Drive folder may pull back documents owned by customers, not your company, without any model-level awareness that a boundary has been crossed.

Barndoor data protection addresses this at the inspection layer: it can detect PII, sensitive customer data, and key-value pairs in MCP responses, and apply transformation or redaction rules before the data enters the model’s context.

Barndoor MCP Governance controls which servers agents can reach; Barndoor data protection controls what those servers are allowed to return.