Governing AI Apps and the MCP Servers They Connect To From One Dashboard

As AI adoption surges, organizations face challenges governing the proliferation of AI apps and the unmanaged MCP servers employees use. Learn how to centralize AI governance with Bifrost and Bifrost Edge for comprehensive control and visibility.

The rapid adoption of AI across enterprises has brought unprecedented efficiency, but it also introduces complex governance challenges. Employees routinely use AI tools and connect to Model Context Protocol (MCP) servers without formal oversight, creating "shadow AI" and significant security and compliance risks. Addressing this requires a unified approach that brings both AI application usage and the underlying MCP server interactions under a single pane of glass. Bifrost, an open-source AI gateway from Maxim AI, provides the core control plane, which is then extended to every endpoint by Bifrost Edge for comprehensive governance.

The Rise of Shadow AI and Ungoverned Endpoints

The proliferation of generative AI tools means employees are increasingly using AI in their daily workflows, often without IT approval. Approximately 67% of employees use AI tools at work, yet only 18% of organizations have formal AI security policies in place. This disparity creates a significant "shadow AI" problem, where sensitive data, including personally identifiable information (PII) and intellectual property, can be exposed. PII is exposed in about 65% of shadow AI-related incidents, while intellectual property is exposed in around 40% of incidents.

Beyond consumer-grade AI chat apps, the Model Context Protocol (MCP) allows AI agents to connect to external tools like databases, APIs, and internal systems, enabling powerful autonomous actions. While beneficial for productivity, ungoverned MCP server usage introduces critical security risks. These include sensitive data exfiltration, unauthorized actions from compromised tool responses, overprivileged agent access, and a lack of audit trails connecting agent actions to human accountability. Many organizations lack comprehensive visibility into how employees use AI, with some reports indicating only 25% have such insight.

Centralized AI Governance with the AI Gateway

An AI gateway functions as a centralized control plane for all AI traffic between applications and LLM providers. It intercepts every request and response, enforcing policies, routing decisions, authentication, and compliance controls. Bifrost, as an AI gateway, offers a robust set of features to establish this central governance:

Virtual Keys: These serve as the primary governance entity, allowing administrators to set per-consumer access permissions, budgets, and rate limits for AI usage.
Routing and Failover: Intelligent routing directs requests to specific models, providers, and keys, ensuring automatic failover in case of provider outages and optimizing performance and cost.
Guardrails: Content safety guardrails can be configured to catch sensitive information like secrets or PII before it leaves the organization's network, supporting compliance standards like SOC 2, GDPR, HIPAA, and ISO 27001.
Audit Logs: Immutable audit logs provide a clear record of all AI interactions, which is crucial for accountability and regulatory compliance.

These controls are configured centrally within the Bifrost AI gateway, establishing a foundational layer of security and policy enforcement for traffic explicitly routed through it.

Extending Governance to Every Machine with Bifrost Edge

While the AI gateway provides robust control for configured traffic, shadow AI persists because many endpoint applications and MCP servers bypass the gateway entirely. This is where Bifrost Edge extends the gateway's governance to every machine in the organization. Bifrost Edge is a lightweight agent that runs on employee macOS, Windows, and Linux devices, routing all AI traffic through the organization's Bifrost AI gateway. This ensures that the same virtual keys, budgets, guardrails, and audit logs configured in the gateway apply to all AI traffic originating from endpoints, regardless of the application used.

Bifrost Edge addresses the core challenge of shadow AI by making endpoint AI usage observable and enforceable from a single dashboard, without requiring users to reconfigure individual applications.

Governing AI Applications at the Endpoint

Bifrost Edge gives administrators granular control over which AI applications are permitted within the organization. Teams can define policies to allow or block specific AI tools, and Edge enforces these decisions directly on each device. When Edge detects a new, unapproved application, it can trigger an approval workflow in the admin console, enabling security teams to review and either approve or deny its use across the fleet. This ensures that only sanctioned applications, fully governed by Bifrost's policies, can operate on company machines. When an application is blocked, users receive clear notifications, preventing potential data exfiltration or policy violations.

Gaining Visibility and Control Over MCP Servers

A significant blind spot for many organizations is the unmanaged proliferation of MCP servers that AI agents connect to. Edge closes this gap by providing a live, fleet-wide inventory of all MCP servers configured within AI applications on endpoint devices. Administrators gain unprecedented visibility into which external tools are being used, by whom, and across how many machines.

Once identified, administrators can make per-server allow or deny decisions. A denied MCP server cannot be used, even if an application previously had it configured. This active enforcement prevents agents from connecting to potentially malicious or unvetted external tools, mitigating risks like supply chain exposure and unauthorized command execution. Edge supports discovery for leading AI applications such as Claude Code, Claude Desktop, Gemini CLI, OpenCode, Codex, and Cursor.

Enforcing Security and Guardrails Everywhere

With Bifrost Edge, the robust security guardrails configured in the Bifrost AI gateway automatically apply to endpoint AI traffic. This means that prompts and responses from desktop apps, browser AI, and coding agents are protected by the same rules that secure gateway traffic. Guardrails can detect and prevent the leakage of sensitive content, such as secrets or PII, before it leaves the machine.

These guardrails include native secrets detection (backed by Gitleaks), custom regex patterns for organization-specific redaction, and integrations with third-party solutions like AWS Bedrock Guardrails, Azure Content Safety, and Patronus AI. This comprehensive approach ensures that security policies are consistently applied across all AI interactions, from the data center to the user's laptop.

Streamlined Deployment and Administration

Bifrost Edge is designed for enterprise-scale deployment. Instead of manual installation on individual machines, organizations can push the Edge agent to every device through existing Mobile Device Management (MDM) platforms. Supported MDM solutions include Jamf, Microsoft Intune, Kandji, Omnissa Workspace ONE, and JumpCloud, covering macOS, Windows, and Linux endpoints.

A managed configuration ensures that devices are pre-pointed at the organization's Bifrost instance upon installation, simplifying rollout. After deployment, administrators manage the entire fleet from a central dashboard. This dashboard provides:

Devices Dashboard: A summary of all machines running Edge, including details like hostname, owner, OS, and installed AI apps/MCP servers.
Approvals Dashboard: A deduplicated catalog of discovered AI apps and MCP servers, allowing for fleet-wide approval or denial with clear status (Pending, Approved, Denied).
Configurations: Centralized settings like the organization certificate (required for routing encrypted AI traffic) and policy sync intervals.

This consolidated view transforms shadow AI from an unmanaged risk into observable and enforceable traffic, enhancing overall security posture and compliance.

The Combined Power: AI Gateway + Bifrost Edge

Effective enterprise AI governance demands a unified strategy. The Bifrost AI gateway serves as the indispensable control plane, where virtual keys, budgets, guardrails, and audit logs are defined. Bifrost Edge then extends this same robust governance directly to the endpoint, ensuring that AI apps and the MCP servers they connect to on every employee's machine adhere to organizational policies. This combined approach eradicates shadow AI, providing a single, consistent framework for visibility, security, and compliance across the entire AI landscape, from the data center to the edge device. Teams can finally gain comprehensive control over all AI interactions, fostering responsible AI adoption at scale.