DEV Community: Arjun Mehta

8 Best AI Gateways for Open WebUI and Self-Hosted Chat Interfaces

Arjun Mehta — Thu, 23 Jul 2026 22:17:04 +0000

This guide compares the 8 best AI gateways for Open WebUI and other self-hosted chat interfaces, covering features like routing, load balancing, and governance. Bifrost emerges as the top choice for teams needing enterprise-grade performance, security, and scalability for their self-hosted AI applications.

Self-hosted chat interfaces like Open WebUI provide teams with a powerful, customizable front end for interacting with a wide range of local and remote large language models. As soon as you connect more than one model provider or need to manage access for multiple users, however, the backend complexity grows. An AI gateway solves this by acting as a centralized control plane for all AI traffic. It unifies disparate APIs, manages keys, enforces budgets, routes around outages, and provides a single point of observability.

This comparison examines eight of the best AI gateways that are well-suited for Open WebUI and similar self-hosted AI front ends. The focus is on features that matter for these use cases: broad provider support (including local models via Ollama), routing flexibility, self-hosting capabilities, and performance.

Key Criteria for Evaluating AI Gateways for Open WebUI

When choosing a gateway for a self-hosted chat UI, the most important factors are:

OpenAI API Compatibility: Open WebUI and most other front ends connect to language models via an OpenAI-compatible API. The gateway must provide this interface flawlessly.
Provider Coverage: The gateway should support the models you use today and those you might use tomorrow, from major cloud providers to local self-hosted models.
Routing and Failover: The ability to route requests based on logic (e.g., cost, latency, model capability) and automatically fail over when a provider is down is critical for reliability.
Self-Hosting: For many teams using Open WebUI, data privacy and infrastructure control are paramount. A self-hostable gateway keeps all traffic within your network perimeter.
Performance: Gateway latency adds to the total response time. A high-performance gateway ensures a snappy user experience, especially in multi-turn conversations.
Governance: Features like virtual keys, rate limits, and budgets are essential for managing usage and cost across users and teams.

The Top 8 AI Gateways

Based on these criteria, here are the leading AI gateways for self-hosted chat interfaces.

1. Bifrost

Bifrost is a high-performance, open-source AI gateway written in Go. It's designed for production-scale workloads where latency, reliability, and security are critical. For teams running Open WebUI in an enterprise context, Bifrost provides the robust infrastructure needed to manage complex routing, governance, and compliance requirements.

Best for: Enterprise teams and developers who need a production-grade, self-hostable gateway with minimal latency and deep governance features.

Key Features:

High Performance: Adds only 11 microseconds of overhead per request at a sustained load of 5,000 requests per second, ensuring that the gateway is never the bottleneck.
Unified API: Provides a single, drop-in OpenAI-compatible API for over 1000 models across more than 20 providers, including local models via Ollama.
Advanced Routing & Reliability: Features automatic provider failover, adaptive load balancing, and weighted routing to maintain high availability.
Enterprise Governance: Includes virtual keys for granular access control, per-key budgets and rate limits, and detailed audit logs for compliance.
Semantic Caching: Reduces costs and improves latency by caching responses based on semantic meaning, not just exact string matches.
MCP Gateway: Bifrost serves as a native Model Context Protocol (MCP) gateway, enabling agentic workflows by allowing models to securely connect to and execute external tools.

The combination of raw performance and enterprise-focused governance makes Bifrost the top choice for organizations that treat their self-hosted AI interfaces as mission-critical applications. The gateway's architecture is built for the moment AI moves from an experiment to core infrastructure.

2. LiteLLM

LiteLLM is a widely adopted open-source gateway and Python SDK that provides a unified OpenAI-compatible interface for over 100 LLM providers. Its ease of use and extensive provider support make it a popular choice for developers and teams getting started with multi-model environments.

Best for: Python-first teams and individual developers who need a flexible, self-hosted gateway with broad model support for development and prototyping.

Key Features:

Extensive Provider Support: Unifies access to a very large catalog of cloud and open-source models.
Self-Hosted Control: Can be deployed via Docker, giving teams full control over their infrastructure.
Cost Management: Includes features for setting budgets and tracking spending per virtual API key.
Active Community: As a popular open-source project, it benefits from a large and active developer community.

While LiteLLM is an excellent tool for getting started, its Python-based architecture can introduce performance limitations under high-concurrency production loads compared to gateways written in languages like Go.

3. Kong AI Gateway

Kong AI Gateway extends the popular Kong API Gateway with AI-specific capabilities. It's a strong choice for enterprises that have already standardized on Kong for their API management and want to apply similar governance and observability to their LLM traffic.

Best for: Organizations already using Kong Gateway that want to add AI governance into their existing API infrastructure.

Key Features:

Plugin Architecture: Leverages Kong's extensive plugin ecosystem for features like authentication, rate limiting, and request transformation.
Multi-LLM Support: Routes traffic to multiple AI providers through a unified interface.
Observability: Integrates with existing monitoring tools to provide visibility into AI traffic alongside other API traffic.
Enterprise Integrations: Connects with enterprise systems for SSO, RBAC, and other security controls.

4. Cloudflare AI Gateway

Cloudflare AI Gateway is a managed service that provides caching, rate limiting, and analytics for AI applications. It runs on Cloudflare's global edge network, which can significantly reduce latency for users distributed geographically.

Best for: Teams that prioritize edge performance and caching and are comfortable with a managed, cloud-hosted solution.

Key Features:

Global Edge Network: Requests are routed through Cloudflare's network, which can improve response times.
Aggressive Caching: Can dramatically reduce costs for applications with repetitive queries by serving responses from a cache close to the user.
Usage Analytics: Provides a dashboard for tracking requests, tokens, and costs across providers.
Free Tier: The core features are available for free, with costs scaling as usage moves into Cloudflare Workers plans.

The primary trade-off is that it's a managed service, which may not be suitable for teams requiring a fully self-hosted solution for data privacy or compliance reasons.

5. OpenRouter

OpenRouter is a managed API that provides access to a vast number of models from different providers through a single API key and a unified billing system. It's an excellent option for easily experimenting with a wide variety of models without managing multiple provider accounts. Open WebUI can connect to OpenRouter by pointing its OpenAI-compatible API settings to the OpenRouter endpoint.

Best for: Individual developers and small teams who want the widest possible model selection with minimal setup and consolidated billing.

Key Features:

Vast Model Catalog: Offers access to hundreds of models, including new and experimental ones, through a single API.
Pay-as-you-go Billing: Uses a prepaid credit system that covers all providers, simplifying cost management.
Easy Integration: Provides a standard OpenAI-compatible endpoint that works seamlessly with tools like Open WebUI.

Like Cloudflare, OpenRouter is a managed service and does not offer a self-hosting option, making it less suitable for use cases with strict data residency requirements.

6. Envoy AI Gateway

Envoy AI Gateway is a newer entrant that extends the popular Envoy proxy with AI-native capabilities. As a Linux Foundation project, it's designed for Kubernetes-native environments and fits well into existing cloud-native stacks.

Best for: Platform and infrastructure teams standardized on Kubernetes and Envoy who need an open-source, vendor-neutral AI gateway.

Key Features:

Kubernetes-Native: Built to run on Kubernetes and integrates with service mesh patterns.
Extensible: Leverages Envoy's filter architecture for customization.
AI-Specific Routing: Supports advanced features like token-based rate limiting and quota management.
MCP Support: Includes a gateway for Model Context Protocol traffic, enabling tool use for agentic applications.

7. Azure API Management

For teams heavily invested in the Microsoft Azure ecosystem, Azure API Management can be configured to act as a gateway for AI services. It offers robust enterprise features and integrates deeply with other Azure services.

Best for: Enterprises building on Azure that need to apply Azure-native security and governance policies to their AI traffic.

Key Features:

Azure Integration: Natively integrates with Microsoft Entra ID for authentication and Azure Monitor for observability.
Enterprise Policies: Allows for the application of fine-grained security policies, such as token-based rate limiting and semantic response caching.
Load Balancing: Can load balance requests across multiple model deployments for high availability.

8. LLMGateway

LLMGateway is an open-source API gateway that provides a unified interface for routing, managing, and analyzing requests across multiple providers. It can be self-hosted with Docker for full data control.

Best for: Developers looking for a straightforward, self-hostable open-source gateway with core routing and analytics features.

Key Features:

Self-Hosted: Provides a Docker-based setup for deploying on your own infrastructure.
Unified API: Offers an OpenAI-compatible interface for seamless integration.
Usage Analytics: Includes tools to track token usage, costs, and response times to help optimize model usage.

Conclusion: Choosing the Right Gateway

For developers and small teams focused on experimentation and flexibility, open-source options like LiteLLM offer a great starting point with broad model support. Managed services like OpenRouter and Cloudflare AI Gateway provide convenience and performance at the cost of some control.

However, for teams deploying Open WebUI as a serious, production-level application, the gateway becomes a critical piece of infrastructure. In these scenarios, performance, reliability, and governance are paramount. Bifrost stands out as the strongest choice, delivering microsecond-level latency, robust enterprise-grade governance, and the flexibility of a self-hosted, open-source solution. The gateway's capabilities extend beyond simple routing, providing a solid foundation for secure, scalable, and compliant AI applications. This is reinforced by its ability to apply centralized governance and security controls, which Bifrost Edge can extend to AI traffic on employee machines, ensuring that the same policies are enforced from the data center to the desktop.

Ultimately, the right AI gateway aligns with your team's operational capacity, performance requirements, and long-term architectural strategy.

Sources

Enterprise AI Adoption Roadmap: From Pilot to Production

Arjun Mehta — Tue, 14 Jul 2026 15:30:44 +0000

Successfully moving artificial intelligence initiatives from pilot projects to full production deployment requires a strategic roadmap. This guide outlines key phases and considerations for establishing a scalable, secure, and well-governed AI infrastructure, with a focus on solutions like Bifrost for centralized management.

Enterprise artificial intelligence (AI) adoption often begins with promising pilot projects that demonstrate value in controlled environments. The critical challenge then shifts to scaling these initiatives, integrating them into core business processes, and managing them effectively in production. A clear roadmap is essential to navigate the complexities of governance, security, cost, and reliability inherent in production AI deployments. This article explores the phases of a robust enterprise AI adoption roadmap, highlighting the infrastructure and strategies necessary for successful scale.

Phase 1: Strategic Planning and Pilot Success

The initial phase focuses on identifying high-impact use cases that align with business objectives and proving their viability through pilot projects. This requires careful consideration of data availability, ethical implications, and potential ROI.

Organizations typically start by defining a clear AI strategy that outlines objectives, identifies key stakeholders, and establishes metrics for success. Pilot projects are then initiated to validate concepts, demonstrate feasibility, and gather early feedback. These pilots should be contained but representative, allowing teams to learn without committing extensive resources. Key questions at this stage include identifying the target business problem, assessing data readiness, and evaluating the required model capabilities.

A critical early step involves selecting appropriate models and providers. While early pilots might use a single LLM provider, a long-term strategy often involves multi-provider flexibility to mitigate vendor lock-in and optimize costs or performance. Planning for this multi-provider future from the outset can simplify later integration.

Phase 2: Building Scalable Infrastructure and Governance

Once pilot projects demonstrate success, the focus shifts to building the underlying infrastructure to support broader deployment. This phase addresses scalability, reliability, and fundamental governance.

Establishing a robust AI infrastructure involves more than just model deployment. It requires a unified system for managing model access, routing, and monitoring. Enterprises often implement an AI gateway, which acts as a central control plane for all LLM traffic.

The Role of an AI Gateway in Production

An AI gateway helps centralize critical functions for production AI. It typically offers a single API endpoint that unifies access to various LLM providers, abstracting away provider-specific APIs. This simplifies development and allows applications to switch between models or providers without extensive code changes.

For instance, Bifrost, an open-source AI gateway developed in Go, provides a single OpenAI-compatible interface across over 1,000 models from numerous providers. This unified API simplifies integration and allows teams to rapidly switch providers or models based on performance, cost, or availability.

Key capabilities of an AI gateway for scaling include:

Multi-Provider Routing and Failover: Automatically directs requests to the best-performing or most cost-effective provider, with built-in fallbacks to ensure continuous service during outages.
Load Balancing: Distributes requests across multiple API keys or providers to manage traffic volume and prevent rate-limit errors.
Semantic Caching: Reduces costs and latency by intelligently caching responses to semantically similar queries, preventing redundant calls to LLM providers.
Observability: Provides real-time request monitoring, metrics (e.g., via Prometheus), and distributed tracing (e.g., via OpenTelemetry) to give visibility into AI application performance in production.

Establishing Governance and Security

As AI applications move toward production, robust governance and security become paramount. This involves not only managing access to models but also safeguarding sensitive data.

An AI gateway can centralize governance through virtual keys, which allow for granular control over access permissions, budgets, and rate limits for different teams, projects, or users. This ensures cost control and prevents unauthorized access to expensive models.

Furthermore, Bifrost applies governance and security controls centrally, and Bifrost Edge extends that same governance and security to AI traffic on employee machines, with endpoint enforcement on each device. This comprehensive approach addresses "shadow AI" — the ungoverned use of AI tools on corporate devices — by routing all endpoint AI traffic through the central gateway for policy enforcement. Edge helps discover and govern which AI applications and Model Context Protocol (MCP) servers are used across an organization's fleet, enforcing enterprise guardrails and security policies at the device level.

Phase 3: Operationalization and Continuous Improvement

The final phase focuses on the ongoing management, optimization, and evolution of AI applications in production. This involves continuous monitoring, performance tuning, and adapting to new technologies.

Continuous Monitoring and Evaluation

Production AI systems require continuous monitoring to track performance, identify drift, and detect anomalies. An AI gateway's observability features provide critical telemetry, including latency, error rates, and token usage, which are essential for maintaining operational health.

Beyond raw metrics, continuous evaluation of model output quality is crucial. Teams often integrate automated evaluation frameworks into their CI/CD pipelines to ensure that model updates or new prompt versions maintain or improve performance. This can involve both quantitative (e.g., programmatic evaluators) and qualitative (e.g., human-in-the-loop review) methods.

Optimizing Performance and Cost

Production AI deployments must be optimized for both performance and cost. Strategies include:

Dynamic Routing: Continuously evaluating provider performance and pricing to route requests optimally.
Model Tiering: Using smaller, more cost-effective models for simpler tasks and reserving larger, more powerful models for complex queries.
Prompt Engineering: Iteratively refining prompts to improve model efficiency and reduce token consumption.

Lifecycle Management of AI Agents

With the rise of AI agents, managing their lifecycle from development to deployment and continuous operation becomes a core part of the roadmap. This includes robust evaluation, simulation, and observability tools. Tools that can simulate user interactions across various scenarios help evaluate agent behavior before production, while observability platforms provide real-time insights into agent performance and interactions in live environments.

Conclusion

Moving enterprise AI from pilot to production is a multi-stage journey that demands strategic planning, robust infrastructure, and continuous operational rigor. By adopting a comprehensive approach that prioritizes governance, security, scalability, and observability, organizations can successfully integrate AI into their operations. Solutions like Bifrost play a pivotal role in centralizing control, optimizing performance, and extending governance from the core gateway to every endpoint, enabling enterprises to realize the full potential of their AI investments with confidence and compliance.

Sources

Bifrost. Bifrost Docs: Supported Providers.
Bifrost. Bifrost Docs: Automatic Fallbacks.
Bifrost. Bifrost Docs: Semantic Caching.
Bifrost. Bifrost Docs: Observability Overview.
Bifrost. Bifrost Docs: Virtual Keys.

8 Endpoint AI Threats Security Teams Overlook

Arjun Mehta — Thu, 09 Jul 2026 10:20:35 +0000

Security teams often miss critical vulnerabilities introduced by ungoverned AI tools on endpoints. Discover eight common endpoint AI threats, from shadow AI data exfiltration to unmonitored MCP servers, and how an AI gateway + Bifrost Edge approach provides comprehensive governance.

The rapid adoption of AI tools by employees has created a new class of cybersecurity challenges that many organizations are struggling to address. While enterprises invest in AI gateways and infrastructure for sanctioned AI applications, a significant blind spot often persists at the endpoint: the user's desktop, browser, and coding environment. This ungoverned usage, frequently termed "shadow AI," introduces substantial risks that traditional security controls are not designed to detect or mitigate.

Understanding these overlooked vulnerabilities is crucial for establishing robust AI governance. Bifrost, an open-source AI gateway from Maxim AI, provides a centralized control plane for managing AI traffic. When combined with Bifrost Edge, this governance extends directly to every employee machine, ensuring comprehensive security from the datacenter to the device.

The Rise of Endpoint AI and the Shadow IT Challenge

Employees are increasingly integrating generative AI tools into their daily workflows to enhance productivity. This includes using public chatbots for summarization, browser extensions for writing, and AI-powered coding assistants for development. While often well-intentioned, this self-service adoption means AI tools operate outside official IT oversight and approval, creating pathways for sensitive data to leave the organization without an audit trail.

Shadow AI poses distinct challenges compared to traditional shadow IT. AI systems process, store, and potentially learn from the data they receive, which can lead to inadvertent data retention, privacy violations, and the exposure of proprietary information beyond organizational boundaries. The sheer volume of AI usage at the endpoint is significant, with some reports indicating that a high percentage of enterprise generative AI use occurs via unmanaged, non-corporate accounts. This creates a vast attack surface that traditional security measures struggle to cover.

8 Endpoint AI Threats Security Teams Overlook

Effective AI security requires visibility and policy enforcement across prompts, responses, tools, and model access. Here are eight critical endpoint AI threats that demand immediate attention from security teams:

1. Unsanctioned Data Exfiltration via Generative AI

Employees frequently paste sensitive data—such as customer PII, proprietary code, or financial records—into public generative AI services to summarize documents, draft emails, or analyze information. This accidental data leakage, often an incidental part of a user's workflow, poses a significant risk of intellectual property loss, compliance violations (e.g., GDPR, HIPAA, SOC 2), and potential data breaches. Traditional Data Loss Prevention (DLP) tools often miss this vector because the data moves through legitimate web traffic to known AI vendors.

2. Insecure or Unmonitored Model Context Protocol (MCP) Servers

AI applications increasingly connect to external tools via Model Context Protocol (MCP) servers, allowing agents to read files, call APIs, and take actions. Users configuring these connections can inadvertently introduce significant security blind spots. Threats include untrusted third-party servers, credential exposure, excessive permissions, and supply chain risks if malicious MCP components are integrated. A compromised MCP server can lead to unauthorized command execution, data leakage, and privilege escalation.

3. Malicious AI Applications or Browser Extensions

The proliferation of AI-enabled browser extensions and desktop applications introduces supply chain risks. Users might download unvetted tools that appear harmless but are designed to exfiltrate data, inject malicious code, or gain unauthorized access. Attackers can also craft seemingly legitimate tools with deceptive names or subtly alter trusted ones to perform unintended actions, making it difficult for users or traditional security tools to identify the threat.

4. Unapproved AI Models and Provider Bypass

Employees may configure their AI applications to use unapproved models or bypass corporate AI gateways entirely by pointing directly to external providers. This circumvents established policies for model quality, cost control, and data handling, leading to inconsistent outputs, unvalidated decisions, and potential exposure to models with weak security postures or unknown data retention policies. Without endpoint enforcement, an organization's central AI governance framework becomes advisory rather than mandatory.

5. Lack of Audit Trails for Endpoint AI Usage

A significant challenge with ungoverned endpoint AI is the absence of comprehensive audit logs and visibility into user interactions. This creates a critical gap for compliance, incident investigation, and accountability. Without detailed records of prompts, responses, and AI tool usage on individual devices, security teams cannot effectively assess risk, enforce policy, or respond to potential breaches. The inability to trace data flows or trigger alerts for suspicious AI activity on personal accounts further compounds this problem.

6. Budget Overruns and Cost Inefficiencies

The uncontrolled use of external AI services by employees can lead to unexpected and substantial API costs. When each user or team independently consumes various LLM providers, an organization can quickly lose track of spending, resulting in budget overruns and inefficiencies. This problem is exacerbated when users access AI services through personal accounts, where corporate cost controls are entirely absent.

7. Vulnerabilities in AI Coding Agents and IDE Integrations

AI coding assistants, now deeply embedded in developer workflows, introduce several critical security risks. These include insecure code generation, prompt injection attacks against the coding tools themselves, and the leakage of proprietary code and credentials through prompts. Coding agents may also store credentials in predictable plaintext files, making them vulnerable to theft by malware. The ability of these agents to execute commands and access sensitive infrastructure expands the attack surface significantly, often outside the scope of traditional AppSec monitoring.

8. Inconsistent Application of Enterprise Guardrails

Even when robust guardrails are configured at a central AI gateway, they often do not extend to AI usage occurring directly on employee endpoints. This creates a critical security gap where prompts and responses from desktop apps, browser AI, or coding agents bypass content safety filters, PII redaction, or custom regex patterns designed to protect sensitive information. The result is an inconsistent security posture, leaving the organization exposed to prompt injection, data leakage, and harmful outputs at the device level.

Addressing Endpoint AI Threats with an AI Gateway and Bifrost Edge

Tackling these endpoint AI threats requires a unified approach that combines centralized control with endpoint enforcement. The Bifrost AI gateway acts as the central policy engine, where virtual keys, budgets, rate limits, routing, guardrails, and audit logs are configured and enforced.

Bifrost Edge extends this same governance and security to AI traffic on employee machines, with endpoint enforcement on each device. This ensures that the AI tools users actively engage with are also brought under organizational policy. Edge achieves this by:

Governing AI applications: Administrators can define which AI applications are permitted, and Edge enforces these decisions on each device, blocking disallowed apps before data can leave the machine. Approval workflows can automatically request review for newly detected applications.
Controlling MCP servers: Edge inventories MCP servers configured inside AI apps, providing fleet-wide visibility and allowing admins to approve or deny specific servers. This enforcement happens on the device, ensuring that unapproved tools cannot be used.
Applying universal guardrails: Because Edge routes AI traffic through Bifrost, every guardrail already configured at the gateway—from native secrets detection to custom regex for PII—applies automatically to endpoint AI.
Enabling auditability: All AI traffic routed through the Bifrost AI gateway, including that originating from endpoints via Edge, is subject to comprehensive audit logging, ensuring a compliance-grade record of usage for regulatory requirements like SOC 2, GDPR, and HIPAA.
Facilitating fleet deployment: Bifrost Edge is designed for silent, fleet-wide deployment through existing Mobile Device Management (MDM) platforms like Jamf, Microsoft Intune, Kandji, Omnissa Workspace ONE, and JumpCloud, simplifying rollout across an organization.

Bifrost Edge is currently in alpha, enabling organizations to gain early access to comprehensive endpoint AI governance. This combined "AI Gateway + Bifrost Edge" approach provides the visibility and control necessary to mitigate shadow AI risks, ensure compliance, and safeguard sensitive data across the entire AI landscape, from the data center to the user's device.

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Why Banning AI Tools Backfires (and What Governing Them Looks Like Instead)

Arjun Mehta — Wed, 24 Jun 2026 17:20:04 +0000

Attempting to ban AI tools to mitigate risk is a failing strategy that drives usage underground and reduces visibility. A better approach is active governance, which involves discovering AI usage across the organization, enforcing centralized policies through an AI gateway like Bifrost, and extending those controls to every employee's machine.

The initial corporate reaction to generative AI was predictable: ban it. Citing risks of data leakage, compliance violations, and intellectual property exposure, many organizations blocked access to popular AI tools, assuming prohibition was the safest path. This strategy, however, does not work. The productivity gains from AI are too significant for employees to ignore, and access is too easy. Banning AI tools does not stop their use; it just drives it into the shadows, creating a far greater risk than managed usage ever could. This is why security and platform teams are shifting from prohibition to active governance, using tools like Bifrost, an open-source AI gateway, to enable innovation safely.

The Inevitable Rise of Shadow AI

"Shadow AI" is the use of AI applications and services without official IT approval, visibility, or oversight. It is the direct consequence of restrictive policies that clash with employee needs for efficiency. When employees are told not to use a tool that helps them work faster and better, they often find ways to use it anyway, turning to personal accounts and devices.

This is not a fringe activity. A 2026 Salesforce survey found that 67% of employees now use AI at work, yet only 18% of companies have formal security policies in place. This gap between adoption and governance creates significant risks:

Data Leakage: Employees may paste sensitive information—customer PII, internal financial data, or proprietary source code—into public AI models. These actions can lead to compliance violations under frameworks like GDPR, HIPAA, or the EU AI Act.
Lack of Visibility: When usage is hidden, security teams have no way to monitor what data is being sent to which models, making it impossible to enforce policies or conduct audits.
Inconsistent Outputs: Without centralized control, different teams may use different models with varying levels of accuracy and safety, leading to inconsistent and unreliable business outcomes.

An outright ban gives leadership a false sense of security while actively increasing the organization's attack surface. The real solution is not to block AI, but to make its use visible, manageable, and secure.

From Prohibition to Governance: A Better Approach

Effective AI governance is not about saying "no." It is about creating a framework that enables employees to use powerful tools safely. This approach is built on three core principles: visibility, control, and enablement. Instead of a blanket ban, a governance strategy allows organizations to understand how AI is being used and to implement guardrails that protect sensitive data while fostering innovation.

This requires a shift in thinking and tooling. A successful program needs a centralized control plane to define policy and a way to extend that policy to every place AI is used, including employee endpoints.

What Effective AI Governance Looks Like in Practice

A modern AI governance framework turns abstract principles into concrete technical controls. This typically involves a multi-layered approach that starts with visibility and progressively adds layers of policy enforcement.

Step 1: Gain Fleet-Wide Visibility

You cannot govern what you cannot see. The first step is to get a comprehensive inventory of AI usage across the entire organization. This means discovering not just which cloud applications are in use, but which desktop clients, browser-based tools, and coding agents employees are running on their local machines.

This is where an endpoint agent becomes critical. A tool like Bifrost Edge runs on every employee machine (macOS, Windows, and Linux) and inventories all AI applications and Model Context Protocol (MCP) servers in use. This data is aggregated into a central dashboard, giving security and platform teams a real-time view of the organization's AI footprint.

Step 2: Enforce Centralized, Granular Policies

Once usage is visible, the next step is to enforce policy from a single control plane. An AI gateway is the natural place for this. The Bifrost AI gateway acts as this central point of control, allowing administrators to define and apply consistent rules to all AI traffic.

Key governance capabilities include:

Application Control: Create explicit allow/deny lists for AI applications discovered by the endpoint agent. Permitted tools continue to function seamlessly, while unauthorized apps are blocked.
Virtual Keys and Budgets: Use virtual keys to assign granular permissions, spending limits, and rate limits to different teams, projects, or users. This prevents runaway costs and ensures resource allocation aligns with business priorities.
Guardrails: Implement content safety policies to detect and redact sensitive data like PII or API keys before they leave the network. Bifrost integrates with providers like AWS Bedrock Guardrails and Azure Content Safety to enforce these rules automatically.

Step 3: Secure AI Usage at the Endpoint

A gateway can only enforce policies on traffic that is routed through it. Shadow AI thrives by bypassing this central chokepoint. The final and most critical step is to extend the gateway's policies to the endpoint, ensuring that the AI tools employees actually use are governed.

This is the combined "AI Gateway + Bifrost Edge" model. Bifrost Edge is the enforcement arm that runs on each laptop. It transparently intercepts traffic from desktop apps like Claude and ChatGPT, browser-based AI, and coding agents, and routes it through the central Bifrost gateway. This means the same virtual keys, budgets, and guardrails defined in the gateway are applied to all AI activity, regardless of its origin. The rollout can be managed across the entire fleet using MDM platforms like Jamf or Intune.

The Benefits of Governing, Not Banning

Shifting from a policy of prohibition to one of active governance provides numerous advantages:

Reduced Security Risk: By making all AI usage visible and routing it through a governed path, organizations eliminate the blind spots created by shadow AI.
Improved Compliance Posture: Centralized logging, auditing, and data redaction provide the evidence needed to meet regulatory requirements for frameworks like SOC 2, HIPAA, and GDPR.
Safe Innovation: Employees get to use the best-in-class tools that make them more productive, while the organization maintains control over data security and costs.

Conclusion: Enable Innovation, Don't Inhibit It

Banning AI tools is an attempt to solve a modern problem with an outdated strategy. It is an approach that is not only unenforceable but actively counterproductive, as it pushes risk into unmonitored channels. The most effective path forward is to embrace AI by governing it intelligently.

By combining the central policy engine of an AI gateway with the endpoint enforcement of an agent, organizations can gain complete visibility and control over their AI ecosystem. Teams evaluating how to manage AI tool usage can request a Bifrost demo or review the open-source repository to see how this unified approach works in practice.

Sources

How to Discover Unsanctioned AI Apps on Employee Laptops

Arjun Mehta — Wed, 24 Jun 2026 17:19:43 +0000

[Discovering unsanctioned or "shadow" AI applications running on employee devices is a critical first step toward effective AI governance. Endpoint visibility tools, including agents like Bifrost Edge, can inventory AI apps and Model Context Protocol (MCP) servers across a fleet to close this security gap.]

The proliferation of AI tools has created a significant blind spot for enterprise security and IT teams: shadow AI. When employees install and use AI applications on their laptops without corporate approval or oversight, it introduces risks related to data security, compliance, and cost control. A recent report from Cisco highlights that while many organizations attempt to block AI applications, employees often find ways to circumvent these blocks. The first step to managing this risk is discovering what is actually running on the endpoint.

Simply blocking AI domains at the network level is often ineffective. An analysis by the security firm Netskope found that generative AI app usage in enterprises grew by 22.5% in a single two-month period, with employees frequently using personal accounts or less common tools that fly under the radar. Effective discovery requires visibility directly on the device. Tools like Bifrost, an open-source AI gateway from Maxim AI, extend governance to the endpoint with an agent that inventories and controls AI usage at its source.

The Challenge of Endpoint AI Visibility

Traditional network monitoring tools and cloud access security brokers (CASBs) can identify traffic to well-known AI services like ChatGPT or Claude. However, they often miss a large and growing segment of the AI landscape:

Desktop Applications: Standalone applications like Claude Desktop, the ChatGPT desktop app, or AI-native code editors like Cursor operate outside the browser and can be difficult to track with web-centric tools.
Coding Agents: Developers use powerful command-line interface (CLI) agents like Claude Code and Codex CLI that make direct API calls from the terminal.
Model Context Protocol (MCP) Servers: Modern AI tools connect to MCP servers to execute actions and access external data. An organization may have no inventory of which third-party tools its employees have authorized, creating a vector for data exfiltration.

Without endpoint visibility, IT and security teams are effectively blind to these applications. This makes it impossible to enforce acceptable use policies, apply data loss prevention (DLP) rules, or manage costs.

Methods for Discovering Unsanctioned AI

Gaining a complete picture of AI usage across a fleet of devices requires a multi-layered approach, with the most effective methods operating directly on the endpoint.

1. Endpoint Agent Deployment

The most comprehensive solution is an endpoint agent designed for AI governance. An agent resides on each company laptop (macOS, Windows, and Linux) and monitors for AI-related processes and network connections.

For example, the Bifrost Edge agent works as an extension of the Bifrost AI gateway. The gateway serves as the central policy and control plane, while the Edge agent performs discovery and enforcement on each device. This approach provides several advantages:

Live Application Inventory: The agent identifies every supported AI desktop app, browser-based tool, and coding agent installed on the machine. This data is sent to a central admin dashboard, providing a real-time, fleet-wide catalog of AI software.
MCP Server Discovery: Critically, the agent can inspect the configurations of tools like Cursor or Claude Code to discover which MCP servers employees have connected. This closes a major visibility gap in agentic workflows.
From Discovery to Governance: Once an application is discovered, it appears in an approvals dashboard. From there, administrators can explicitly allow or deny its use across the entire organization.

2. Mobile Device Management (MDM) Queries

Mobile Device Management (MDM) platforms like Jamf, Microsoft Intune, or Kandji can be used to query installed applications on managed devices. Administrators can create scripts or policies to scan for the application bundles or executable files associated with known AI tools.

While useful for a baseline inventory, MDM-based discovery has limitations:

Incomplete Picture: It may not detect web-only tools, browser extensions, or dynamically downloaded CLI agents that do not have a standard installation footprint.
Lack of Context: MDM can see that an application is installed, but it cannot see how it is being used, what data it is accessing, or which MCP servers it is configured with.
Delayed Data: Inventories are typically gathered on a periodic basis, so the data may not be real-time.

MDM is most effective when used to deploy and manage a dedicated AI governance agent, ensuring 100% coverage across the fleet. Bifrost Edge, for instance, is designed for silent, large-scale deployment via MDM.

3. Network and Proxy Log Analysis

Analyzing network traffic logs from firewalls, DNS servers, and web proxies can reveal connections to the APIs and domains of AI services. This can help identify usage from both sanctioned and unsanctioned devices on the corporate network.

However, this method is less effective in the era of remote work, where many devices operate primarily off the corporate network. It also struggles with encrypted traffic and cannot distinguish between different activities on a single domain. For example, it can show a connection to anthropic.com, but it cannot differentiate between an employee reading a blog post and using the Claude API in a desktop app.

Building a Fleet-Wide AI Inventory

A dedicated endpoint solution provides the ground truth needed for effective governance. With a tool like Bifrost Edge, an administrator can move from an unknown state to a comprehensive, actionable inventory.

The process typically involves these steps:

Deploy the Agent: The Bifrost Edge agent is pushed to all macOS, Windows, and Linux devices using an existing MDM platform.
Initial Discovery: As agents come online, they automatically populate the admin console with discovered AI apps and MCP servers from across the fleet. The system deduplicates entries, so each unique app or server appears only once for review.
Review and Approve: Security and IT admins review the discovered inventory. They can see how many devices a particular app is installed on to gauge its prevalence.
Set Policy: Admins approve applications that align with company policy and deny those that do not. This decision is then automatically enforced on every device running the agent. Allowed apps have their traffic routed through the central Bifrost gateway to apply security guardrails and cost controls, while denied apps are blocked.

From Discovery to Complete AI Governance

Discovering unsanctioned AI applications is the essential first phase of a broader AI governance strategy. Once visibility is established, organizations can implement controls to manage risk without stifling innovation. The combined AI Gateway + Bifrost Edge model provides a path to achieving this. The gateway acts as the centralized control plane for defining policy—such as configuring virtual keys with budgets or setting up audit logs for compliance—while the endpoint agent ensures those policies are enforced everywhere, for every app.

This approach allows teams to transition from a reactive, block-based posture to a proactive governance model that enables safe and productive AI adoption. Teams looking to get visibility into their fleet's AI usage can request a demo of Bifrost to see how the discovery and governance platform works.

Sources

How Enterprises Can Detect Shadow AI Before It Becomes a Breach

Arjun Mehta — Wed, 24 Jun 2026 17:18:57 +0000

[Shadow AI, the use of unapproved AI tools by employees, creates significant security and compliance risks. A dedicated endpoint governance solution like Bifrost Edge, working with an AI gateway, can detect and govern these tools before they lead to a data breach.]

The use of unapproved applications by employees, often called "shadow IT," is a long-standing challenge for security teams. Its modern equivalent, shadow AI, has emerged as a more acute enterprise risk. When employees use public AI tools like ChatGPT, Claude, and various coding assistants for work, they can inadvertently expose sensitive company data, create compliance gaps, and introduce security vulnerabilities. A 2023 report by Cisco noted that while employees use AI to boost productivity, their organizations often lack visibility into which tools are being used. This ungoverned usage makes it critical for enterprises to find a reliable way to detect shadow AI. One approach is to combine a central AI gateway with an endpoint governance agent. An open-source AI gateway like Bifrost acts as the control plane, while an endpoint agent extends visibility and policy enforcement to every employee's machine.

What is Shadow AI and What Are the Risks?

Shadow AI refers to any AI application, service, or tool used by employees without the organization's explicit approval and oversight. This includes using personal accounts for large language models (LLMs), integrating unvetted AI plugins into browsers, or using AI-powered desktop applications that process corporate data.

The risks associated with shadow AI are substantial:

Data Leakage: Employees may paste proprietary code, customer information, financial data, or strategic plans into public AI tools. This data can be used to train models or may be stored insecurely by the service provider, creating a high risk of exposure.
Compliance Violations: Industries with strict data handling regulations like healthcare (HIPAA) and finance (GLBA) face significant compliance risks. Using unapproved AI tools can violate data residency, privacy, and security requirements, leading to heavy fines. A recent report from KPMG highlights that 68% of executives are concerned about the lack of a clear AI governance program.
Security Vulnerabilities: Unvetted AI tools, especially browser extensions and desktop apps, can have security flaws or malicious code that exfiltrates data or compromises the user's machine. AI applications that connect to external tool servers using protocols like MCP (Model-Context Protocol) can also create new attack surfaces if those servers are not managed.
Lack of Audit Trails: Without centralized governance, there is no record of what data was shared with which AI model, by whom, or when. This makes it impossible to conduct forensic analysis after a security incident or to demonstrate compliance to auditors. The Bifrost AI gateway addresses this by creating immutable audit logs for every request.

The Challenge of Detecting Ungoverned AI

Traditional network monitoring and cloud access security brokers (CASBs) struggle to effectively detect and manage shadow AI. Many AI tools use standard web protocols (HTTPS) that blend in with normal web traffic, making it difficult to distinguish between approved and unapproved usage based on network data alone. Furthermore, with the rise of desktop applications for AI, much of the activity happens on the endpoint, outside the visibility of network-based tools.

Attempting to block popular AI domains at the network firewall is often a losing battle. It is an overly broad approach that can stifle productivity and is easily circumvented by employees using VPNs or personal devices. A more effective strategy requires visibility directly on the endpoint.

An Endpoint-First Approach to AI Governance

A modern solution to shadow AI combines a central policy engine with an endpoint agent that runs on every employee machine. This is the model used by Bifrost, which pairs its AI gateway with an agent called Bifrost Edge.

Here’s how this combined approach works:

AI Gateway as the Control Plane: The Bifrost AI gateway serves as the central point for defining all AI policies. Administrators configure virtual keys with specific budgets, rate limits, and access rules. They also set up security policies, such as guardrails that detect secrets or PII in prompts.
Endpoint Agent for Discovery and Enforcement: The Bifrost Edge agent is deployed to all company laptops and desktops via MDM solutions like Jamf, Intune, or Kandji. The agent runs silently on each machine, inspecting AI traffic from desktop apps, coding assistants, and browsers.
Fleet-Wide Visibility: Edge discovers every AI application and MCP server in use across the entire fleet of devices. This data populates a central admin dashboard, providing the security team with a real-time inventory of all shadow AI tools. This turns an unknown risk into a manageable list.
From Visibility to Control: From the dashboard, administrators can create an application governance policy, explicitly allowing or denying specific tools. They can also govern which MCP servers employees are allowed to connect their AI tools to.
Enforcement on the Device: Once a policy is set, Edge enforces it on each machine. Traffic from approved apps is automatically and transparently routed through the Bifrost gateway, where all the pre-configured security rules, budgets, and audit logging policies are applied. Traffic from denied apps is blocked before it can leave the endpoint.

This model allows organizations to embrace the productivity benefits of AI without sacrificing security or control. Instead of blocking AI altogether, it brings shadow AI usage out of the dark and into a managed environment.

Implementing a Shadow AI Detection Strategy

For enterprises looking to get ahead of the risks, the first step is gaining visibility. Deploying an endpoint governance solution provides an immediate inventory of the AI tools currently in use. Organizations are often surprised by the number and variety of applications employees have adopted.

Once visibility is established, security and IT teams can work with business units to create a formal AI usage policy. The data from the discovery phase informs this policy, ensuring it addresses the tools employees find most valuable. With a platform like Bifrost and Bifrost Edge, that policy can be enforced consistently, from the data center to every employee's laptop.

This endpoint-first approach provides a scalable and effective way to detect, govern, and secure AI usage across the enterprise, preventing shadow AI from escalating into a full-blown data breach. Teams evaluating solutions for AI governance can review the Bifrost documentation or explore its capabilities in the open-source repository.

Sources

Cisco. (2023). 2024 Data Privacy Benchmark Study. https://www.cisco.com/c/dam/en_us/about/doing_business/trust-center/docs/cisco-data-privacy-benchmark-study-2024.pdf
KPMG. (2023). Generative AI risk management. https://kpmg.com/xx/en/home/insights/2023/11/generative-ai-risk-management.html
ISACA. (2023). Shadow IT in the Age of AI. https://www.isaca.org/resources/isaca-journal/issues/2023/volume-6/shadow-it-in-the-age-of-ai

The Real Risks of Shadow AI and a Practical Framework to Govern It

Arjun Mehta — Wed, 24 Jun 2026 17:18:52 +0000

[“Shadow AI” describes the use of AI tools by employees without official approval or oversight, creating significant data security and compliance risks. A practical governance framework involves discovering unmanaged tools, enforcing central policies, and securing data at the endpoint, which solutions like Bifrost and Bifrost Edge are designed to address.]

The proliferation of powerful, easily accessible AI tools has created a significant blind spot for many organizations: shadow AI. This term refers to the use of AI applications, models, and services by employees without the knowledge, approval, or governance of their IT and security departments. While often adopted to improve productivity, this ungoverned usage introduces substantial risks, including data leakage, compliance violations, and increased security vulnerabilities. Addressing this requires a shift from simply banning tools to implementing a comprehensive governance framework. One such approach involves using a combination of an AI gateway like Bifrost, an open-source AI gateway from Maxim AI, and an endpoint agent to extend visibility and control directly to employee workstations.

What is Shadow AI and Why is it a Problem?

Shadow AI encompasses any AI system or tool used for business purposes that is not part of the organization's officially sanctioned technology stack. This includes employees using public-facing web-based chatbots like ChatGPT or Claude for work-related queries, developers installing powerful coding assistants in their IDEs, or teams connecting unvetted third-party tools to their AI models.

The core problem is the lack of oversight. A 2023 report noted that a significant percentage of workers who use generative AI for their jobs have input sensitive company data into these tools. When employees use unmanaged AI, the organization has no visibility into what data is being shared, which models are being used, or what security measures are in place. This creates several specific, high-stakes risks.

Key Risks of Ungoverned AI Usage

Data Leakage and Confidentiality Breaches: Employees may inadvertently paste proprietary code, customer PII, financial data, or strategic plans into public AI tools. This data can be used to train future models, potentially exposing it to other users.
Compliance and Regulatory Violations: Industries with strict data handling requirements like healthcare (HIPAA) or finance (PCI DSS), along with regions governed by regulations like GDPR, face severe penalties if sensitive data is processed by non-compliant AI services. Shadow AI makes it impossible to ensure and audit compliance.
Security Vulnerabilities: Ungoverned AI tools, especially those that can execute code or connect to other systems via protocols like MCP (Model Context Protocol), can introduce security risks. A malicious or poorly secured tool could be a vector for attacks or data exfiltration.
Inaccurate or Biased Outputs: Without proper vetting, teams may rely on AI models that produce inaccurate, biased, or low-quality results, leading to poor business decisions, flawed code, or reputational damage.
Cost Overruns: While many AI tools have free tiers, heavy or enterprise-level usage can incur significant costs that are not tracked or managed centrally, leading to unpredictable expenses.

A Practical Framework for Governing Shadow AI

An effective response to shadow AI is not about restricting innovation but about enabling it safely. This requires a framework built on three pillars: Discovery, Governance, and Security. This approach brings unmanaged AI usage out of the shadows and under the organization's central policy engine.

1. Discover: Gaining Fleet-Wide Visibility

You cannot govern what you cannot see. The first step is to establish comprehensive, real-time visibility into the AI tools being used across all company devices. An endpoint agent is essential for this, as it can monitor activity at the source.

A solution like Bifrost Edge is designed for this purpose. Deployed to employee machines via MDM platforms like Jamf or Microsoft Intune, it inventories all AI-related activity. This includes:

Installed AI Applications: Identifying every instance of desktop AI apps like Claude Desktop, ChatGPT, and Cursor across the fleet.
MCP Server Connections: Detecting which external tools and services, exposed via MCP, are configured within employees' coding agents and other apps. This closes a critical visibility gap, as MCP servers can access local data and execute actions.
Web-Based AI Usage: Monitoring traffic to popular web-based AI services.

This discovery process provides a complete catalog of AI usage, forming the foundation for an informed governance strategy rather than one based on assumptions.

2. Govern: Enforcing Centralized Policies

Once you have visibility, the next step is to enforce policy. The goal is to ensure that all AI usage, regardless of the tool or user, routes through a central control plane where rules can be applied consistently. This is the role of an AI gateway.

The Bifrost gateway acts as this central policy engine. When combined with an endpoint agent like Bifrost Edge, the same rules configured at the gateway are extended to every device. This combined "AI Gateway + Bifrost Edge" model enables several layers of control:

App and Tool Approval: Admins can create explicit allow/deny lists for all discovered AI applications and MCP servers. A denied application is blocked on the employee's machine before it can transmit any data.
Unified Access Control: All AI requests are funneled through the gateway and authenticated using virtual keys. This allows for granular control, with specific budgets, rate limits, and model access rights assigned per user, team, or project.
Centralized Audit Trail: Every prompt and response from every tool on every machine is routed through the gateway, creating a comprehensive, immutable audit log. This is critical for compliance and incident investigation.

3. Secure: Applying Consistent Guardrails

The final pillar is securing the data that flows through these tools. By routing all endpoint AI traffic through a central gateway, organizations can apply a consistent set of security controls to every request, regardless of its origin.

These controls, often called guardrails, are configured once in the gateway and enforced everywhere. Bifrost's enterprise guardrails, for example, can be used to:

Detect and Redact Sensitive Data: Use pattern matching (regex) or secrets detection to find and remove API keys, PII, and other confidential information from prompts before they leave the network.
Enforce Content Safety: Integrate with services like AWS Bedrock Guardrails or Azure Content Safety to block harmful or inappropriate content.
Control Data Egress: By routing traffic through an in-VPC gateway deployment, organizations can ensure that sensitive data never traverses the public internet.

This layered approach allows security teams to manage risk centrally while giving employees the freedom to use approved, governed AI tools that make them more productive.

From Shadow Risk to Governed Innovation

Shadow AI represents a serious and growing risk, but it is also a signal of unmet business needs. Employees adopt these tools because they provide real value. A successful strategy acknowledges this reality by providing a secure and governed path for AI adoption. By implementing a framework of discovery, governance, and security—powered by a unified control plane like an AI gateway extended to the endpoint—organizations can turn the risk of shadow AI into a well-managed engine for innovation. Teams evaluating solutions can request a Bifrost demo or review the open-source repository to learn more.

Sources

What is Shadow AI and Why It’s a Fast-Growing Enterprise Risk

Arjun Mehta — Wed, 24 Jun 2026 17:16:34 +0000

Shadow AI is the use of ungoverned AI tools by employees, creating significant security and compliance risks. An AI gateway like Bifrost combined with an endpoint agent provides the necessary visibility and control to mitigate this threat.

Shadow AI refers to the use of artificial intelligence tools, models, and applications within an organization without the knowledge or oversight of IT, security, and compliance teams. It is the direct successor to "shadow IT," where employees used unapproved cloud services like Dropbox or Slack to be more productive. Today, that same behavior has shifted to AI. Employees are using public AI chatbots, coding assistants, and browser extensions to get work done faster, creating a massive, unmonitored layer of risk. Bifrost, an open-source AI gateway from Maxim AI, is one of the platforms designed to bring this ungoverned usage into a managed framework.

The scale of the problem is substantial. Recent studies indicate that a large majority of employees, as many as 78% in some surveys, use AI tools at work that are not formally approved by their employer. This isn't malicious behavior; it is driven by a desire for efficiency. However, it creates significant blind spots for data security, regulatory compliance, and intellectual property protection.

The Business Risks of Shadow AI

When AI usage happens outside of official channels, it bypasses the controls that protect an organization. This introduces several critical risks that compound as adoption scales.

Data Leakage and IP Loss: This is the most immediate risk. Employees frequently copy and paste sensitive information into public AI tools, from proprietary source code and financial data to customer PII and strategic documents. A notable incident at a major tech company involved employees pasting confidential code into ChatGPT, resulting in model outputs that resembled the company's internal data. Without visibility, organizations cannot know what data is leaving their environment or how third-party AI providers might use it for model training.
Compliance and Regulatory Violations: Many industries are governed by strict data handling regulations like GDPR, HIPAA, and SOC 2. Shadow AI usage creates a direct path to non-compliance. Using an unvetted AI tool to process customer data can violate GDPR's data processing requirements or HIPAA's rules on Protected Health Information (PHI). Upcoming regulations like the EU AI Act will impose significant fines for non-compliant AI systems, making audibility and governance essential.
Expanded Security Attack Surface: Unsanctioned AI tools, especially browser extensions and coding assistants, can create new vectors for attack. These tools may have security vulnerabilities, route data through insecure endpoints, or require overly permissive access to other applications, exposing the organization to breaches.
Lack of Audit Trails and Visibility: When an incident occurs, security and compliance teams rely on audit logs to understand the scope of the damage. Shadow AI operates without any centralized logging, leaving teams with no visibility into which users accessed which tools or what data was shared. Only a small fraction of organizations report having comprehensive visibility into employee AI use.

Common Examples of Shadow AI in the Enterprise

Shadow AI is not an abstract threat; it is present in daily workflows across every department. The accessibility of modern AI tools means anyone can incorporate them into their work, often without realizing the risk.

Desktop and Web Applications: Employees regularly use powerful desktop apps like Claude Desktop and ChatGPT, or their web-based counterparts, to summarize meetings, draft documents, and analyze data. Each query can potentially send sensitive internal information to a third-party service.
Coding and CLI Agents: Developers are among the earliest adopters, using tools like Claude Code, Codex CLI, and Gemini CLI to write, debug, and document code. This workflow directly exposes source code, API keys, and system architecture details to external models.
Browser Extensions: AI-powered browser extensions that summarize pages, compose emails, or assist with research are common. These extensions often have broad permissions to read web page content, creating a silent and persistent data egress point that is difficult to track.
SaaS-Embedded AI: Many sanctioned SaaS platforms are now embedding AI features. While the platform itself may be approved, the embedded AI features might operate under different data policies, creating a form of "nested" shadow AI that is even harder to detect.

The Solution: AI Gateway and Endpoint Governance

Blocking all AI tools is not a viable strategy; it stifles productivity and often fails as employees find workarounds. The effective approach is to move from prohibition to governed enablement. This requires a two-part solution that combines a central control plane with endpoint enforcement.

This is the model offered by platforms like Bifrost, which pairs a powerful AI gateway with an endpoint agent called Bifrost Edge.

The AI Gateway as a Control Plane: The Bifrost AI gateway acts as the central policy engine for all AI traffic. It is where organizations define and enforce critical governance rules. This includes creating virtual keys to manage access, setting budgets and rate limits, and configuring security guardrails to detect and block sensitive data. The gateway provides a complete audit log of all governed requests, which is essential for compliance.
Bifrost Edge for Endpoint Enforcement: A gateway alone only governs traffic that is explicitly configured to pass through it. To solve the Shadow AI problem, that governance must be extended to every employee's machine. Bifrost Edge is an agent that runs on macOS, Windows, and Linux devices and automatically routes all AI traffic—from desktop apps, browsers, and CLIs—through the central Bifrost gateway.

This combined "AI Gateway + Bifrost Edge" approach provides comprehensive visibility and control. Administrators get a fleet-wide dashboard showing which AI applications are in use and can create app-level policies to allow or deny specific tools. The same goes for MCP servers used by advanced coding agents. Because policies are enforced on the device, a denied application is blocked before any data can leave the machine.

Deployment across an enterprise is handled through existing device management platforms like Jamf or Intune, allowing for a silent, scalable rollout that brings all endpoint AI usage under the same governance framework that protects officially sanctioned applications.

Getting Started with AI Governance

Shadow AI is no longer an emerging issue; it is a present and growing risk inside most organizations. Ignoring it creates significant exposure, while simply banning it is ineffective. The path forward involves establishing clear visibility, creating sensible policies, and deploying technology that enables safe, productive AI use.

Teams evaluating solutions to manage Shadow AI can request a Bifrost demo or review the open-source repository to learn more about a gateway-based approach to governance.