DEV Community: Katarina Hoffmann

10 Best AI Gateways for Python Teams

Katarina Hoffmann — Thu, 23 Jul 2026 22:20:55 +0000

[This guide compares the top AI gateways for Python developers, evaluating them on performance, provider support, and enterprise features. For teams building production-grade AI applications, Bifrost is the leading choice due to its low latency, comprehensive governance, and native Python integrations.]

Managing API keys, routing requests, and controlling costs for large language models (LLMs) can introduce significant complexity into Python applications. An AI gateway acts as a centralized layer to manage these infrastructure concerns, providing a single, consistent interface to multiple AI providers. For Python teams, this means less boilerplate code for handling provider-specific SDKs, better reliability through automatic failover, and centralized control over security and spending.

This article evaluates ten of the best AI gateways available today, with a focus on their suitability for Python-based AI development. The comparison covers open-source tools and managed services, highlighting key features like model support, performance, and enterprise-readiness. The top options, including Bifrost, an open-source AI gateway from Maxim AI, offer unified APIs that simplify integration and improve application resilience.

Key Criteria for Evaluating AI Gateways

When selecting an AI gateway for a Python project, engineering teams should evaluate several key factors:

Python SDK and Integration: How easily does the gateway integrate with a Python codebase? Look for native Python SDKs or drop-in compatibility with popular libraries like OpenAI's client.
Performance and Latency: The gateway should add minimal overhead. Low-latency routing is critical for real-time applications.
Provider and Model Support: A good gateway supports a wide range of LLM providers (OpenAI, Anthropic, Google, etc.) and allows for routing to specific models.
Reliability Features: Core capabilities should include automatic failover to a healthy provider during an outage and load balancing to distribute traffic.
Governance and Security: Look for features like virtual API keys, per-user budgets, rate limiting, and audit logs to control access and spending.
Deployment Flexibility: The ability to self-host in a private cloud (VPC) or on-premises is crucial for organizations with strict data privacy and security requirements.

The Top 10 AI Gateways

Based on the criteria above, here is a detailed comparison of the best AI gateways for Python teams.

1. Bifrost

Bifrost is a high-performance, open-source AI gateway written in Go, designed for enterprise-scale workloads. It unifies access to over 1,000 models from more than 20 providers through a single OpenAI-compatible API.

For Python developers, Bifrost acts as a drop-in replacement for the OpenAI SDK. Integration requires changing only the base URL in the client configuration, with no other code modifications needed. Its performance is a key differentiator, with published benchmarks showing only 11 microseconds of added latency per request under a load of 5,000 requests per second.

Key Features:

Reliability: Provides automatic fallbacks and intelligent load balancing across multiple API keys and providers.
Governance: Offers virtual keys for granular access control, per-user budgets, and rate limits.
Semantic Caching: Reduces costs and improves latency by caching responses to semantically similar queries.
MCP Gateway: Includes a built-in Model Context Protocol (MCP) gateway to connect models with external tools and agents.
Enterprise-Ready: Bifrost Enterprise offers features like high-availability clustering, audit logs for compliance, and guardrails for content safety. Centralized governance and security controls can be extended to employee machines with Bifrost Edge, which provides endpoint enforcement for all AI traffic.

Best for: Enterprise Python teams that require high performance, scalability, and comprehensive governance for mission-critical AI applications. Its ability to be deployed in a VPC or on-premises makes it suitable for regulated industries.

2. LiteLLM

LiteLLM is a popular open-source library that provides a unified interface for calling over 100 LLM APIs. It can be used as a standalone proxy server, making it a lightweight AI gateway. Its primary strength is its simplicity and broad model support.

Python integration is native, as LiteLLM is itself a Python library. It offers a consistent completion() function that works across all supported models.

Key Features:

Unified API: Provides a simple, consistent format for text, image, and audio inputs across all providers.
Fallback and Retry Logic: Supports routing fallbacks and automatic retries with exponential backoff.
Cost Tracking: Includes built-in utilities for tracking spending per API call.
Streaming Support: Natively handles streaming responses from providers.

Best for: Startups and development teams looking for a simple, open-source solution to manage multi-provider LLM access directly within their Python environment.

3. Kong AI Gateway

The Kong AI Gateway is an extension of the widely used Kong API Gateway. It brings enterprise-grade API management capabilities to AI workloads, focusing on control, security, and observability for AI services.

It is not Python-specific but works with any application that makes HTTP requests. Kong provides a control plane for applying policies to AI traffic, regardless of the client's programming language.

Key Features:

Multi-LLM Support: Routes requests to various open-source and proprietary models.
AI-Specific Plugins: Offers plugins for prompt engineering, response caching, and credential management.
Observability: Provides real-time logging and analytics for AI requests.
No-Code Configuration: Allows platform teams to configure AI policies without modifying application code.

Best for: Large organizations that have already invested in the Kong ecosystem and need to extend their existing API management practices to include LLM APIs.

4. Cloudflare AI Gateway

Cloudflare AI Gateway is a managed service that provides caching, rate limiting, and analytics for AI applications. As part of the Cloudflare ecosystem, it leverages a global network to reduce latency and improve reliability.

It works as a proxy, so any Python application can be configured to send requests through it. The setup involves changing the API endpoint to the one provided by Cloudflare.

Key Features:

Global Caching: Caches responses at the edge to reduce latency for repeated requests.
Analytics and Logging: Offers a dashboard to monitor requests, errors, costs, and users.
Rate Limiting: Protects applications from denial-of-service attacks and unexpected cost spikes.
Provider Agnostic: Works with any LLM provider that has a REST API.

Best for: Teams already using Cloudflare's network services who want a simple, managed solution for adding caching and basic analytics to their AI applications.

5. OpenRouter

OpenRouter is a managed service that aggregates access to a wide variety of LLM and image generation models, including new and experimental ones. It offers a single API endpoint and manages the complexity of integrating with different provider APIs.

It provides an OpenAI-compatible API, making it easy to use with the standard Python OpenAI client by changing the base_url.

Key Features:

Model Variety: Provides access to a long tail of models not always available through other gateways.
Pay-per-Use: Users pay a single bill for usage across all models, simplifying cost management.
Community-Ranked Models: Shows leaderboards of models based on user ratings.
Built-in Moderation: Offers optional NSFW filters.

Best for: Developers and researchers who need to experiment with a diverse range of models and prefer a simple, managed pay-as-you-go service.

6. Databricks AI Gateway (formerly MLflow AI Gateway)

The Databricks AI Gateway is a component of the Databricks platform that provides a unified endpoint for querying, managing, and deploying LLMs. It is tightly integrated with the Databricks ecosystem, including MLflow for model tracking.

Integration is primarily designed for use within the Databricks environment using Python notebooks and jobs.

Key Features:

Unified Interface: Provides a single API for accessing and managing different LLM providers.
Credential Management: Securely stores and manages API keys within Databricks.
Rate Limiting and Cost Control: Enforces granular access policies and monitors usage.
MLflow Integration: Automatically logs requests and responses for evaluation and governance.

Best for: Data science and machine learning teams already working within the Databracks ecosystem.

7. Amazon Bedrock

While not strictly a gateway in the same sense as others on this list, Amazon Bedrock serves a similar function by providing a single API to access a range of foundation models from providers like Anthropic, Cohere, and Amazon itself.

It is accessed via the AWS SDK for Python (Boto3), offering a managed and secure way to use different models without managing individual provider relationships.

Key Features:

Managed Service: A fully managed AWS service, simplifying deployment and scaling.
Model Choice: Provides access to a curated set of high-performing models.
Security and Compliance: Integrates with AWS security services like IAM and VPC.
Customization: Allows teams to privately fine-tune models with their own data.

Best for: Organizations heavily invested in the AWS cloud that want a fully managed, secure way to access a curated selection of foundation models.

8. Google Vertex AI Model Garden

Similar to Bedrock, Google's Vertex AI provides a unified platform to access, tune, and deploy a wide variety of foundation models, including Google's own Gemini models. It offers a single API endpoint for interacting with this "Model Garden."

The platform is accessed using Google Cloud's Python SDK, making it a natural choice for teams developing on GCP.

Key Features:

Extensive Model Catalog: Access to Google models and a wide range of third-party and open-source models.
Managed Infrastructure: Handles all the operational overhead of hosting and scaling models.
Tooling Integration: Tightly integrated with other Google Cloud services for MLOps and data processing.
Search and Conversational AI: Specialized tools for building RAG and agent-based applications.

Best for: Development teams building on Google Cloud Platform who need access to a diverse set of models within a managed, integrated environment.

9. NVIDIA NIM

NVIDIA NIM is a set of microservices that are part of the NVIDIA AI Enterprise software platform. It allows teams to deploy optimized AI models on their own infrastructure. While it focuses on self-hosted model inference, it can act as a gateway to these models, providing a standardized API.

NIM provides an OpenAI-compatible API, allowing Python applications to interact with self-hosted models using the standard client.

Key Features:

Optimized Inference: Delivers high-performance inference for models running on NVIDIA GPUs.
Self-Hosted: Provides full control over data privacy and model deployment.
Broad Model Support: Supports a curated set of community and NVIDIA-built models.
Enterprise Support: Comes with enterprise-grade support from NVIDIA.

Best for: Companies with on-premises GPU infrastructure and a need for high-performance, self-hosted inference with full data control.

10. Azure AI Studio

Azure AI Studio is Microsoft's platform for building AI applications, which includes a gateway to access models from OpenAI, Mistral, and other providers. It acts as a central hub for managing AI development on Azure.

It integrates with the Azure SDK for Python, allowing developers to consume models deployed through the studio.

Key Features:

Model Catalog: Access to models from Microsoft, OpenAI, and the open-source community.
Responsible AI Tools: Includes built-in tools for content safety and responsible AI best practices.
Managed Endpoints: Simplifies the process of deploying and scaling models.
Azure Integration: Deeply integrated with the broader Azure cloud ecosystem.

Best for: Enterprise development teams that are standardized on the Microsoft Azure stack.

Conclusion: Making the Right Choice

Choosing the right AI gateway depends on a team's specific needs, scale, and existing infrastructure. For Python teams requiring maximum performance, control, and enterprise-grade governance, an open-source, self-hostable solution like Bifrost is a compelling option. Its low-latency architecture and extensive feature set for reliability and security make it an ideal control plane for production AI services.

For teams looking for simplicity and rapid experimentation, managed services like OpenRouter or lightweight libraries like LiteLLM offer a faster path to multi-provider integration. Ultimately, the best AI gateway is one that removes infrastructure complexity, allowing Python developers to focus on building innovative AI features. Teams evaluating AI gateways can request a Bifrost demo or review its open-source repository to explore its capabilities further.

Why 80% of Enterprise AI Projects Stall (and How to Avoid It)

Katarina Hoffmann — Tue, 14 Jul 2026 15:34:45 +0000

Many enterprise AI initiatives face significant hurdles, with a large percentage failing to move beyond pilot stages. This post examines key reasons for enterprise AI project stalls—from governance to evaluation—and outlines strategies to ensure successful deployment and value realization, identifying how proper infrastructure and evaluation platforms can help.

The promise of artificial intelligence to transform enterprises is undeniable, yet a significant challenge persists: a large majority of enterprise AI projects fail to deliver their intended business value or scale beyond initial pilots. Various studies from the RAND Corporation, Gartner, BCG, and MIT's Project NANDA consistently report that between 70% and 95% of enterprise AI projects ultimately stall or fail to achieve measurable ROI. For generative AI specifically, some research indicates up to 95% of pilot projects fail to deliver measurable returns. Understanding the underlying causes of this widespread stagnation is crucial for organizations aiming to move their AI initiatives from proof-of-concept to impactful production.

This analysis explores the key reasons why so many enterprise AI projects stall, and how robust strategies, supported by dedicated AI infrastructure and evaluation platforms, can help organizations navigate these common pitfalls.

The Hard Truth: High Failure Rates in Enterprise AI

Despite substantial investments—projected at $1.5 trillion on AI in 2025—the gap between AI potential and operational reality remains wide. Surveys indicate that 42% of companies abandoned most of their AI initiatives in 2025, a dramatic increase from 17% in the previous year. The average organization scrapped nearly half of its AI proof-of-concepts before reaching production. This indicates that the problem is not merely technical, but systemic and organizational.

The RAND Corporation's 2024 study, based on interviews with experienced data scientists, identified several root causes, most of which are organizational rather than purely technical. These include misunderstood problem definitions, inadequate data, a technology-first mentality, insufficient infrastructure, and applying AI to overly difficult problems.

Root Cause 1: Lack of Robust AI Governance and Security

One of the most significant inhibitors to scaling AI in the enterprise is the absence of comprehensive governance and security frameworks. As AI adoption outpaces governance, organizations struggle to build controlled pathways for AI, leading to projects stalling when risk committees cannot verify security controls or legal teams cannot sign off on audit trails.

A major component of this challenge is "shadow AI"—the use of public, unsanctioned AI tools by employees for work-related tasks, operating outside organizational policies. This creates significant blind spots and exposes sensitive data.

80% of organizations report moderate to pervasive shadow AI use.
Only 25% have comprehensive visibility into how employees use AI.
Security breaches linked to shadow AI are common, with 20% of organizations experiencing such incidents.
These breaches often involve exposure of personally identifiable information (65%) and intellectual property (40%).
Critical risks include data leakage to AI providers and regulatory non-compliance.

Effective governance requires clear ownership, operational processes, and a proactive approach to managing AI at every stage. For example, the Bifrost AI gateway acts as a control plane for AI traffic, centralizing governance with features like virtual keys, budgets, rate limits, and audit logs. This infrastructure helps enforce policies across disparate LLM providers. Beyond the gateway, Bifrost Edge extends this same governance to the endpoint, running on employee machines to route all AI traffic (desktop apps, browser AI, coding agents, and MCP servers) through the organization's Bifrost. This approach helps to end shadow AI by bringing all endpoint AI usage under policy without requiring users to manually reconfigure their applications. Bifrost Edge ensures that the same guardrails and security controls configured in the gateway apply everywhere, providing crucial endpoint enforcement and visibility. Teams can, for example, leverage Bifrost Edge to gain visibility into which AI applications and MCP servers are being used, and then make per-app or per-server allow/deny decisions that are enforced directly on the device. The Bifrost AI gateway, an open-source AI gateway, centralizes these controls.

Root Cause 2: Ineffective Evaluation and Quality Assurance

Many AI projects fail to transition from proof-of-concept to production due to a lack of robust testing and evaluation frameworks. Traditional software testing methods are often insufficient for complex AI systems, especially large language models (LLMs) and autonomous agents.

Half of enterprises have deployed an AI agent or LLM feature that passed internal evaluations but still caused a customer-facing failure.
The most common reason for distrusting automated evaluation is poor alignment with real-world outcomes.
Enterprises often struggle with defining comprehensive metrics that capture the nuances of language generation, factual accuracy, and practical utility, going beyond basic precision and recall.

The inability to accurately measure an AI system's performance, identify biases, or predict its behavior in real-world scenarios leads to unreliable outcomes and a lack of confidence from business stakeholders. This is particularly challenging for multi-step AI agents, which can make several individually plausible decisions and still reach an incorrect or undesirable result.

Platforms like Maxim AI address this by providing an end-to-end evaluation, simulation, and observability framework. Maxim AI's simulation engine allows teams to test agents across hundreds of real-world scenarios and user personas, observing how agents respond at every step of a conversation. Its evaluation framework supports both machine and human evaluations, with off-the-shelf and custom evaluators configurable at the session, trace, or span level, ensuring granular quality measurement. This comprehensive approach helps bridge the "evaluation gap," where agent autonomy outpaces assurance.

Root Cause 3: Deployment Complexity and Scalability Challenges

Moving an AI pilot to enterprise-wide production involves significant technical hurdles that often go unaddressed during initial experimentation. These include integrating AI solutions into legacy systems, managing infrastructure costs, and ensuring models perform reliably at scale.

Many organizations underestimate the infrastructure and computational costs associated with running large models.
The need for robust deployment strategies, including failover, load balancing, and secure data access, becomes critical at scale.

Deploying LLMs at enterprise scale requires specialized infrastructure. A powerful AI gateway like Bifrost centralizes LLM traffic, providing essential features for production environments. Bifrost supports automatic failover between providers and intelligent load balancing to ensure high availability and optimal performance, even across different API keys and models. Its unified API simplifies integration with existing applications by allowing developers to swap out SDKs with a single base URL change.

For enterprises with strict security and compliance requirements, Bifrost offers features such as clustering for high availability and zero-downtime deployments, data access control for granular permissions, and in-VPC deployments to keep data within private cloud infrastructure. These capabilities are crucial for transitioning from a successful pilot in a controlled environment to a large-scale enterprise production implementation, overcoming what is often referred to as "pilot purgatory".

Root Cause 4: Misaligned Expectations and Unproven ROI

Many AI projects begin with enthusiasm but falter when they fail to demonstrate clear business value or an acceptable return on investment. This often stems from:

Unclear problem definition: Stakeholders may miscommunicate the specific problem AI needs to solve, leading to solutions that do not align with operational needs.
Technology-first mentality: Organizations often select AI tools based on hype rather than a clear problem-solution fit.
Lack of measurable KPIs: Without defined metrics for success, teams struggle to quantify the impact of AI initiatives or justify further investment.

Projects often fail not because the technology itself is flawed, but because there is no clear path to proving its worth to the business. This leads to what some describe as "the silent shelf-deploy"—a model goes live but sees minimal usage, eventually being retired quietly.

To avoid this, a clear business case and defined success metrics must be established before project inception. Maxim AI's observability platform plays a vital role in demonstrating ROI by tracking, debugging, and resolving live quality issues with real-time alerts. It enables in-production quality measurement through automated evaluations based on custom rules, allowing teams to continuously monitor agent performance and align it with business outcomes. The platform's custom dashboards provide deep insights across agent behavior, enabling organizations to optimize their agentic systems and make data-driven decisions on further AI investments. This visibility helps ensure that AI initiatives are not just technically impressive, but genuinely helpful and impactful.

Strategies to Avoid Stalling: A Proactive Approach

For organizations to successfully deploy and scale AI projects, they must adopt a proactive strategy that addresses these common pitfalls:

Prioritize Governance First: Establish robust AI governance policies and security frameworks from the outset. Implement tools like Bifrost and Bifrost Edge to centralize control, enforce policies across all AI traffic—including endpoint usage—and mitigate shadow AI risks. This ensures compliance and provides audit trails necessary for enterprise adoption.
Invest in Continuous Evaluation and Observability: Move beyond basic testing with comprehensive evaluation platforms. Utilize tools like Maxim AI to simulate real-world scenarios, conduct detailed evaluations (both human and machine), and gain real-time observability into production AI systems. This enables continuous quality improvement and proactive issue resolution.
Plan for Production from Day One: Design AI projects with deployment and scalability in mind, not just proof-of-concept success. Leverage high-performance AI gateways like Bifrost for resilient deployment, automatic failover, and efficient routing across multiple LLM providers. Ensure integration with existing systems and plan for infrastructure costs.
Define Clear Business Value and Metrics: Articulate the specific business problem AI will solve and define measurable key performance indicators (KPIs) before starting a project. Use evaluation and observability platforms to continuously track these metrics and demonstrate tangible ROI to stakeholders. Maxim AI's dashboards can provide the transparency needed to justify investment and scale successful initiatives.

By addressing these systemic challenges with strategic planning and the right technological foundation, enterprises can significantly increase their chances of moving AI projects from the "stalled" category to successful, impactful production.

Sources

Talyx. (2026, January 27). Why 90% of Enterprise AI Implementations Fail.
Optro. (2026, June 2). Shadow AI stats for 2026: The hidden adoption gap defining enterprise risk.
Technology Radius. (2026, May 23). 20 Shadow AI Statistics 2024–2026: Enterprise AI Risk.
WorkOS. (2025, July 22). Why most enterprise AI projects fail — and the patterns that actually work.
The Data Experts. (2025, September 11). Enterprise AI Failure Rate: Why 95% of Projects Fail.

Best Tools to Govern AI Across Managed and BYOD Devices

Katarina Hoffmann — Thu, 09 Jul 2026 10:24:12 +0000

Enterprises grappling with ungoverned AI usage on employee devices require robust solutions. This article explores the challenges of shadow AI and reviews the leading tools for comprehensive endpoint AI governance, highlighting Bifrost + Bifrost Edge as a top recommendation.

The proliferation of AI applications, from sophisticated coding assistants to everyday chat interfaces, has introduced a significant challenge for IT and security teams: governing AI usage on employee devices. Whether company-managed laptops or personal "bring your own device" (BYOD) setups, these endpoints often become blind spots, creating what is known as "shadow AI." This ungoverned usage poses compliance risks, exposes sensitive data, and leads to unmanaged costs. Organizations increasingly need dedicated tools to extend AI governance from centralized gateways directly to the devices where AI interaction actually happens. Bifrost, an open-source AI gateway from Maxim AI, along with its Bifrost Edge endpoint agent, offers a comprehensive approach to this critical problem.

The Challenge of Endpoint AI Governance

AI applications, particularly large language models (LLMs), have moved beyond specialized roles to become ubiquitous productivity tools. Employees use them for coding, writing, research, and data analysis. While this adoption can boost efficiency, it often occurs outside of corporate IT visibility and control.

This "shadow AI" problem manifests in several ways:

Data Leakage: Employees may inadvertently input proprietary information, trade secrets, or sensitive customer data into public or unapproved AI models, leading to potential data breaches and compliance violations (e.g., GDPR, HIPAA).
Compliance Risks: Without audit trails or policy enforcement on endpoint AI usage, organizations struggle to demonstrate compliance with industry regulations and internal governance frameworks.
Security Vulnerabilities: Ungoverned AI tools might be used with weak authentication, expose API keys, or become vectors for novel phishing and social engineering attacks.
Cost Overruns: Uncontrolled API calls from endpoint-based AI tools can accumulate significant and unexpected expenses for companies if linked to corporate accounts or if API keys are compromised.
Lack of Visibility: IT and security teams often have no inventory of which AI applications or Model Context Protocol (MCP) servers are in use across their device fleet, making it impossible to enforce any policy.

Traditional endpoint security solutions, such as Data Loss Prevention (DLP) or network proxies, often struggle to provide granular, AI-specific governance. They might block broad categories of traffic but lack the intelligence to differentiate between approved and unapproved AI model access, or to apply guardrails specifically to prompt and response content.

Key Criteria for Evaluating AI Governance Solutions

Effective endpoint AI governance requires solutions that are both comprehensive and user-friendly. When evaluating tools, organizations should consider:

Comprehensive Coverage: The solution must govern all forms of AI usage on a device, including desktop applications (e.g., Claude Desktop, Cursor), browser-based AI (e.g., ChatGPT web), and command-line coding agents (e.g., Claude Code, Gemini CLI).
Centralized Policy Management: Policies (access control, budgets, guardrails) should be defined once at a central control plane and then enforced consistently across all endpoints.
Transparent Enforcement: The solution should ideally operate without requiring users to manually reconfigure each AI application, ensuring transparent and automatic governance.
Visibility and Auditability: Administrators need a real-time inventory of all AI applications and MCP servers in use across the fleet, coupled with immutable audit logs for compliance.
Data Security and Guardrails: Capabilities to inspect prompts and responses for sensitive data, PII, or policy violations, with the ability to redact or block content before it leaves the device.
MDM-Native Deployment: For enterprise-wide adoption, seamless deployment and management via existing Mobile Device Management (MDM) platforms (e.g., Jamf, Intune) are crucial.
Support for BYOD: The solution should extend governance effectively to BYOD devices without overly intrusive controls that might violate user privacy.

Bifrost + Bifrost Edge: Comprehensive Endpoint AI Governance

For organizations seeking a robust and unified solution for endpoint AI governance, Bifrost, the AI gateway, combined with its Bifrost Edge endpoint agent, stands out as a leading choice. This integrated approach ensures that AI traffic, whether originating from enterprise applications or user-initiated tools on managed and BYOD devices, adheres to organizational policies.

The Bifrost AI gateway serves as the central control plane, where virtual keys, budgets, rate limits, routing policies, and security guardrails are configured. Bifrost Edge then extends this same governance directly to every machine. This combined narrative ensures consistency: the policies defined centrally at the gateway are actively enforced at the endpoint.

Key Capabilities:

Transparent Traffic Routing: Bifrost Edge runs as an agent on macOS, Windows, and Linux devices, automatically routing all AI traffic through the organization's Bifrost gateway. This means users do not need to change base URLs or reconfigure individual applications; governance is applied transparently upon installation.
Unified AI Application Governance: Administrators gain fleet-wide visibility into which AI applications are being used. Bifrost Edge allows IT to approve or deny specific AI apps (e.g., Claude Desktop, ChatGPT, Cursor) from a central dashboard. Any denied application is blocked from sending traffic to LLMs, ensuring compliance.
MCP Server Governance: A unique strength of Bifrost Edge is its ability to inventory and govern Model Context Protocol (MCP) servers. As AI tools increasingly connect to external MCP servers for capabilities like file access or API calls, Edge provides visibility into these connections and allows administrators to approve or deny specific MCP servers, enforcing security at the tool level.
Guardrails Everywhere: Because traffic flows through Bifrost, every guardrail configured at the gateway automatically applies to endpoint AI usage. This includes native secrets detection, custom regex for PII, and integrations with enterprise guardrail providers like AWS Bedrock Guardrails and Azure Content Safety. Prompts and responses are inspected and protected before sensitive data leaves the device.
MDM-Native Deployment: For large-scale rollouts, Bifrost Edge is designed for fleet-wide deployment via Mobile Device Management (MDM) platforms such as Jamf, Microsoft Intune, Kandji, Omnissa Workspace ONE, and JumpCloud. This enables silent installation and pre-configuration, simplifying enterprise adoption.
Comprehensive Visibility and Audit Logs: The Bifrost admin console provides dashboards to monitor all devices running Edge agents, installed AI applications, and configured MCP servers. All traffic is subject to immutable audit logs, crucial for SOC 2, GDPR, HIPAA, and ISO 27001 compliance.
Alpha Status: Bifrost Edge is currently in alpha, with teams registering for onboarding, indicating active development and a focused approach to a complex problem.

Implementing Comprehensive Endpoint AI Governance

For organizations ready to tackle shadow AI and implement robust governance, the following steps are crucial:

Assess Current AI Usage: Gain an understanding of which AI tools employees are already using. This might involve surveys, initial network traffic analysis, or pilot deployments of an endpoint agent.
Define AI Policies: Establish clear policies regarding approved AI applications, data types that can be shared with AI, and specific guardrails for sensitive information.
Choose a Dedicated Solution: Select a platform designed for AI governance across both the gateway and endpoints. A solution like Bifrost + Bifrost Edge provides this unified control.
Plan for MDM Deployment: Leverage existing MDM infrastructure for silent, fleet-wide deployment of endpoint agents, ensuring minimal user interruption and consistent rollout.
Educate Users: Inform employees about AI usage policies, the rationale behind them, and how the new tools help protect company data while enabling productive AI use.
Monitor and Iterate: Continuously monitor AI usage, review audit logs, and refine policies and guardrails based on real-world patterns and evolving threats.

Conclusion

The challenge of governing AI across managed and BYOD devices is a pressing concern for modern enterprises. Relying on traditional security tools alone leaves significant gaps in visibility, compliance, and data protection. Dedicated solutions that extend AI governance directly to the endpoint are essential.

The combination of the Bifrost AI gateway and the Bifrost Edge endpoint agent provides a powerful and comprehensive answer. By offering centralized policy management, transparent endpoint enforcement, deep visibility into AI applications and MCP servers, and robust guardrails, it enables organizations to mitigate the risks of shadow AI effectively. Teams seeking to establish clear, auditable, and secure AI usage across their entire device fleet should consider evaluating this approach. Teams evaluating AI gateways can request a Bifrost demo or review the open-source repository.

Sources

The Rise of Shadow AI and What It Means for Your Business. (2024). Retrieved from https://www.forbes.com/sites/forbescommunicationscouncil/2024/02/09/the-rise-of-shadow-ai-and-what-it-means-for-your-business/?sh=229199a531f9
Shadow AI: What It Is, Why It's a Threat, and How to Prevent It. (2023). Retrieved from https://www.helpsystems.com/blog/shadow-ai-what-it-why-its-threat-and-how-prevent-it
Bifrost Edge: Endpoint AI Governance. Retrieved from https://www.getmaxim.ai/bifrost/edge
Govern AI Apps - Bifrost Docs. Retrieved from https://docs.getbifrost.ai/edge/app-governance
Guardrails - Bifrost Docs. Retrieved from https://docs.getbifrost.ai/enterprise/guardrails

A CISO's Playbook for Surfacing Every AI Tool Already in Use

Katarina Hoffmann — Wed, 24 Jun 2026 17:26:58 +0000

A CISO's guide to discovering shadow AI across the enterprise. This playbook covers how to surface every AI tool in use, from web apps to coding agents, using a combination of policy, network analysis, and endpoint governance tools like Bifrost Edge.

The widespread adoption of generative AI has created a significant blind spot for security leaders: employees are using hundreds of ungoverned AI tools for daily work. A recent report from the analyst firm Enterprise Technology Research (ETR) highlights that while 70% of organizations are increasing their AI budgets, many lack the visibility to manage the associated risks. This "shadow AI" ecosystem, spanning everything from web-based chatbots to integrated development environment (IDE) plugins and desktop applications, introduces unmanaged pathways for data exfiltration, compliance violations, and intellectual property loss.

For Chief Information Security Officers (CISOs), the first step toward managing this risk is creating a comprehensive inventory of every AI tool already in use. An open-source AI gateway like Bifrost can centralize and govern known AI traffic, but it cannot see the tools that bypass it. This playbook provides a structured approach to surfacing that hidden usage and bringing it under a unified governance framework.

The Challenge: Why Shadow AI Is Hard to Find

Shadow AI thrives because it is decentralized and user-driven. Unlike traditional software that requires formal procurement and deployment, modern AI tools are often free, browser-based, or installed with a single click. This creates several discovery challenges.

Endpoint Proliferation: AI is no longer confined to the data center. It runs on employee laptops inside desktop apps like Claude Desktop and Cursor, as web apps like ChatGPT, and as coding agents directly in the command line.
Encrypted Traffic: Most AI services use standard HTTPS, making their traffic difficult to distinguish from general web browsing using network-level tools alone.
Dynamic and Evolving Tools: The AI tool landscape changes weekly. New models and applications appear constantly, making it impossible to maintain a static, manually curated list of indicators of compromise or blocked domains.
Model Context Protocol (MCP) Servers: Modern coding agents connect to external MCP servers to execute tools and access local files. These connections are a powerful, yet often invisible, vector for data movement that most security tools are not designed to inspect.

Stage 1: Initial Discovery and Baseline

The initial goal is to build a baseline understanding of AI usage without deploying heavy-handed blocking, which can drive usage further into the shadows.

Conduct User Surveys and Policy Reviews

Start with the human layer. Anonymous surveys can provide valuable, honest feedback on which tools teams find most useful and for what purposes. This is also the time to review and update the company's acceptable use policy to explicitly address generative AI. The NIST AI Risk Management Framework (AI RMF 1.0) provides a solid foundation for developing these policies, emphasizing the need to "Map, Measure, and Manage" AI risks.

Analyze Network and Proxy Logs

While not a complete solution, analyzing DNS requests and proxy logs can reveal connections to the most common AI service domains. Create a list of top-level domains for services like OpenAI, Anthropic, Google AI, and others. This method will catch the low-hanging fruit but will miss desktop applications that may use different endpoints or less obvious services that bundle AI capabilities.

Stage 2: Automated Discovery with Endpoint Governance

Manual methods and network analysis provide an incomplete picture. To get a definitive, real-time inventory, CISOs need a solution that provides visibility directly on the endpoint, where the tools are being used. This is where an endpoint governance agent becomes critical.

The Bifrost AI gateway provides the central control plane for setting policy, and Bifrost Edge extends that policy to every employee machine. This combination moves a security program from reactive analysis to proactive governance.

How Endpoint Governance Works

An endpoint agent like Bifrost Edge is deployed to every company-managed device via an existing mobile device management (MDM) solution like Jamf, Intune, or Kandji. Once installed, it operates transparently to the user, inspecting traffic and identifying connections to known AI services and, crucially, discovering new ones.

Key capabilities for discovery include:

Application Inventory: The agent identifies every installed AI-native application, such as the ChatGPT or Claude desktop apps, and reports them back to a central dashboard.
MCP Server Discovery: It inspects traffic from coding agents like Claude Code and Codex CLI to discover and inventory every MCP server they are configured to use. This closes a major visibility gap for engineering teams.
Browser AI Visibility: The agent can identify traffic to web-based AI tools, distinguishing it from general browsing.

From Discovery to a Fleet-Wide Inventory

The output of this stage is not just a list of domains but a rich, fleet-wide catalog of every AI application and MCP server in use, tied to specific devices and users. A centralized admin dashboard provides a single view to see what is running where. This inventory becomes the foundation for a risk-based governance strategy.

Stage 3: Implementing Risk-Based Governance

With a comprehensive inventory in hand, security teams can move from discovery to control. The goal is not necessarily to block every tool but to enforce consistent security and compliance policies on the tools that are approved for use.

Create an Approval Workflow

Using the discovered inventory, CISOs can implement a formal approval workflow for all AI tools.

Review Discovered Tools: Analyze the inventory to identify tools that offer clear business value versus those that pose an unacceptable risk.
Approve or Deny: Mark each application and MCP server as approved or denied. This decision is then automatically enforced on the endpoint. Approved tools continue to function seamlessly, while denied tools are blocked before they can transmit data.
Route and Govern: All traffic from approved tools is automatically routed through the organization's central Bifrost AI gateway. This ensures that every request is subject to the organization's data loss prevention policies, guardrails, and audit logging.

The "AI Gateway + Bifrost Edge" Model

This combined approach is the most effective way to manage AI risk at scale. The Bifrost AI gateway acts as the central policy engine and enforcement point for all known and sanctioned AI traffic. Bifrost Edge acts as the discovery and enforcement agent on the endpoint, ensuring that even previously unknown "shadow AI" is either blocked or brought into compliance with the gateway's policies. This creates a closed-loop system where nothing is left ungoverned.

Next Steps: Building a Sustainable Program

Surfacing the AI tools already in use is the foundational step in a modern AI governance program. By moving from manual spot-checks to a continuous, automated discovery and enforcement model, CISOs can enable their organizations to adopt AI safely and effectively. This approach turns a major security blind spot into a well-managed and visible component of the enterprise software ecosystem.

Teams looking to implement such a playbook can start by evaluating how an endpoint governance solution can provide the necessary visibility. The information needed to manage AI risk is already on the network and endpoints; the key is having the right tools to surface it. Teams evaluating AI gateways and endpoint governance can request a Bifrost demo to see this model in action.

Sources

Blocking vs. Governing AI: Why the Difference Decides Your Security Posture

Katarina Hoffmann — Wed, 24 Jun 2026 17:26:29 +0000

The choice between blocking and governing AI tools defines an organization's security posture. Outright bans often push usage into unmonitored "shadow AI," increasing risk, while a governance-first approach using platforms like Bifrost enables security, compliance, and productivity by providing visibility and control.

The rapid adoption of generative AI has presented IT and security leaders with a critical dilemma. With employees using AI tools for everything from coding to market research, the default response for many organizations is to block access. The logic is simple: if you remove the tool, you remove the risk. However, this strategy often backfires, creating more significant security gaps than it closes.

The reality is that employees, driven by a need for productivity, will find ways to use these tools, whether they are officially sanctioned or not. This leads to a phenomenon known as "shadow AI," where usage is completely invisible and unmanaged by security teams. A more effective and sustainable approach is not to block AI, but to govern it. Platforms such as Bifrost, an open-source AI gateway, are designed to provide the visibility and control necessary to implement a governance-first strategy.

The Flawed Logic of Blocking AI

Blocking access to AI tools at the network level seems like a straightforward security win. It prevents employees from pasting sensitive information into public models and appears to create a clear, defensible boundary. In practice, this approach is brittle and counterproductive for several reasons:

It Fails to Stop Usage: Studies show that a significant percentage of employees will find workarounds if a tool they find useful is blocked. They switch to personal devices, mobile hotspots, or use unsanctioned accounts, effectively moving their activity outside of any corporate visibility.
It Creates "Shadow AI": When usage is hidden, it cannot be monitored or controlled. Security teams have no insight into which tools are being used, what data is being shared, or which employees are creating risk. This invisible attack surface is far more dangerous than known, managed usage.
It Stifles Innovation: Competitors are using AI to accelerate product development, improve customer service, and increase operational efficiency. An outright ban on AI tools can put a company at a significant competitive disadvantage.
It Erodes Trust: A blanket ban can signal a lack of trust in employees and create a culture where security is seen as a barrier to productivity rather than an enabler.

Understanding Shadow AI

Shadow AI refers to the use of AI applications and services by employees without the knowledge or approval of the IT and security departments. It's the modern evolution of shadow IT, but the risks are amplified. While shadow IT often involved unauthorized data storage like a personal cloud account, shadow AI involves data processing by third-party models, creating new vectors for data leakage and compliance violations.

Recent reports highlight the scale of the problem:

As many as 67% of employees now use AI tools at work, yet only a small fraction of organizations have formal security policies to manage them.
A significant number of organizations have already experienced security incidents linked directly to generative AI tools.
Sensitive data, including personally identifiable information (PII) and intellectual property, is frequently exposed in shadow AI incidents.

This gap between rapid adoption and slow governance is where the most significant risks lie. Without visibility, organizations cannot enforce data handling policies, manage compliance with regulations like GDPR or HIPAA, or prevent the leakage of source code and strategic documents.

The Governance Approach: See, Define, Enforce

An AI governance framework shifts the goal from preventing access to managing it responsibly. It's a strategy of controlled enablement that balances security requirements with business productivity. Effective governance is built on three pillars:

Visibility: The foundational step is to see what's actually happening. This requires a mechanism to discover every AI tool being used across the organization, from desktop applications and web clients to coding agents in a developer's terminal.
Policy: Once usage is visible, security teams can define and apply a clear AI usage policy. This isn't just a document; it's a set of enforceable rules. The policy should specify which applications are approved, which are denied, and the conditions under which they can be used.
Enforcement: A policy is only effective if it can be enforced. This requires a control point that can actively block or allow traffic based on the defined rules, ensuring that the organization's security posture is maintained in real-time.

Implementing AI Governance at the Endpoint

The most effective place to implement AI governance is at the endpoint: the employee's machine. This is where AI usage happens. Relying on network-level controls alone is insufficient, as workarounds are simple. An endpoint-first approach ensures that policies are applied to every application, on any network.

This is where a solution like the combination of an AI Gateway and Bifrost Edge becomes critical. The Bifrost gateway acts as the central policy engine where administrators define the rules. Bifrost Edge is an agent deployed on each employee machine that extends those rules to the endpoint.

This architecture enables a robust governance model:

Comprehensive Application Discovery: The endpoint agent can identify all AI applications in use, including desktop clients like ChatGPT and Claude, web-based tools, and developer-focused CLI agents.
Granular Application Control: Based on this discovery, administrators can create and enforce allow/deny lists. Approved applications can function seamlessly, while unauthorized tools are blocked before they can transmit data. Details on this approach can be found in documentation on app governance.
Visibility into Agentic Workflows: Modern AI tools often use the Model Context Protocol (MCP) to connect to external tools and servers. An endpoint agent can discover and govern these MCP connections, preventing agents from connecting to unapproved or malicious external services.
Centralized Policy Enforcement: The security rules, guardrails, and audit logging capabilities configured in the central AI gateway are automatically enforced on every device. This ensures consistent application of security policy across the entire organization.

This model allows for a fleet-wide rollout using standard Mobile Device Management (MDM) platforms, making it possible to secure thousands of devices with a single, centrally managed policy.

From Security Risk to Strategic Advantage

By shifting from a strategy of blocking to one of governing, organizations turn a significant security risk into a strategic advantage. This approach allows businesses to:

Strengthen Security Posture: Gaining full visibility into AI usage eliminates the blind spots created by shadow AI, allowing for proactive risk management.
Enable Productivity Safely: Employees can use powerful, approved AI tools to innovate and work more efficiently without compromising sensitive data.
Ensure Continuous Compliance: With comprehensive audit logs and enforced data policies, organizations can demonstrate compliance with industry and data privacy regulations.

The question for security leaders is no longer if AI will be used in their organization, but whether that usage will be managed or unmanaged. Blocking AI creates an illusion of security while driving risk into the shadows. Governing AI provides the visibility and control needed to secure the modern enterprise.

Sources

Reducing Shadow AI Risk Without Killing Employee Productivity

Katarina Hoffmann — Wed, 24 Jun 2026 17:25:05 +0000

The rapid adoption of unmanaged AI tools creates significant shadow AI risk for enterprises. An AI gateway like Bifrost combined with an endpoint agent provides the visibility and control needed to secure AI usage without blocking employee productivity.

The use of unapproved technology in the workplace, or "shadow IT," has long been a challenge for security teams. The recent explosion of generative AI tools has created a new, more complex variant: shadow AI. Employees, aiming to be more productive, are adopting AI applications for everything from code generation to summarizing confidential meetings, often without IT approval or oversight. This practice, while usually well-intentioned, introduces significant security, compliance, and financial risks. A central AI governance strategy, starting with a control plane like the Bifrost open-source AI gateway, is the foundation for managing this risk. However, a gateway alone cannot see the AI running on employee endpoints.

The Hidden Costs of Unmanaged AI Tools

Shadow AI is more than just unauthorized software; it is an unmanaged expansion of an organization's attack surface and data footprint. When employees use public AI tools, they may inadvertently expose sensitive corporate data, intellectual property, and customer information.

Key risks include:

Data Leakage: Corporate data fed into public AI models can be used for training or may be stored in jurisdictions that do not meet the company's security standards, creating a critical risk of data exfiltration.
Compliance Violations: The use of unsanctioned tools can lead to violations of data protection regulations like GDPR and HIPAA, resulting in failed audits and substantial fines.
Intellectual Property Loss: Proprietary source code, product roadmaps, and financial models processed by external AI tools can be compromised, undermining competitive advantage.
Lack of Visibility and Control: Without a central view of AI usage, IT and security teams are governing in the dark. They cannot enforce security policies, manage costs, or make informed decisions about AI investments.
Inaccurate Outputs: AI tools can produce plausible but factually incorrect information, which can lead to poor business decisions or reputational damage if used in external communications.

Why Outright Bans on AI Tools Backfire

The initial reaction of many organizations to shadow AI was to ban public AI tools entirely. While this may seem like a simple solution, it is often counterproductive. Employees turn to these tools because they provide a genuine productivity boost, helping them automate routine tasks and focus on higher-value work.

An outright ban often leads to:

Reduced Productivity: Preventing access to useful tools can slow down innovation and frustrate employees who are trying to work more efficiently.
Circumvention: Determined employees will find ways around bans, using personal devices or networks, which pushes shadow AI further into the dark and makes it even harder to manage.
A Negative Security Culture: A prohibitive stance can create an adversarial relationship between employees and security teams, discouraging open communication about new tools and potential risks.

The goal should not be to stop AI adoption but to enable it securely. Frameworks like the NIST AI Risk Management Framework provide guidance on managing AI risks responsibly, emphasizing a balanced approach that supports innovation while maintaining trustworthiness and security.

A Better Approach: Visibility and Governance at the Endpoint

A modern approach to shadow AI risk focuses on gaining visibility and applying consistent governance, regardless of where the AI is being used. This is achieved by combining a central AI gateway as a policy control plane with an endpoint agent that extends those policies to every employee's machine.

This "gateway plus endpoint" model allows organizations to embrace the productivity benefits of AI while mitigating the risks. It brings all AI usage, whether from sanctioned applications in the cloud or unmanaged desktop tools, under a single, unified governance framework.

How Bifrost Implements Endpoint AI Governance

Bifrost provides a comprehensive solution for AI governance that addresses the challenge of shadow AI through two integrated components.

The Control Plane: Bifrost AI Gateway

The Bifrost AI gateway serves as the central policy and enforcement point for all configured AI traffic. It is where administrators define the rules of the road for AI usage across the organization. Core governance features are configured here, including:

Virtual Keys: These act as a granular access control layer, allowing teams to set specific budgets, rate limits, and model access permissions for different users, projects, or departments.
Guardrails: Security policies, such as secrets detection and custom regex filters, are configured in the gateway to prevent sensitive data from being sent to or received from AI models.
Audit Logs: The gateway provides an immutable record of all requests, which is essential for compliance and security investigations.

The Endpoint Agent: Bifrost Edge

While the gateway governs known traffic, Bifrost Edge is designed to tackle shadow AI directly on employee devices. Currently in alpha, Bifrost Edge is an agent that runs on macOS, Windows, and Linux machines and extends the gateway's governance policies to cover the AI tools people actually use every day.

Its key capabilities include:

Visibility First: Edge discovers all AI applications and MCP servers being used across the fleet, providing security teams with a complete inventory of AI usage for the first time.
Granular Control: From a central dashboard, administrators can review the discovered tools and make explicit allow or deny decisions. A denied application is blocked on the device, enforcing policy directly at the source.
Seamless for Users: Edge routes AI traffic through the Bifrost gateway transparently. There are no SDKs for users to install or base URLs to change. Governance is applied automatically without interrupting workflows.
Fleet-Wide Deployment: Edge is designed for enterprise environments and can be deployed silently to all company devices using MDM solutions like Jamf, Intune, and Kandji.

The Benefits of Governed AI Adoption

By combining a central AI gateway with an endpoint agent, organizations can move from a reactive, prohibitive stance on AI to a proactive, enabling one. This approach provides the foundation for a secure AI adoption strategy that doesn't sacrifice speed or innovation.

Teams gain a single pane of glass for all AI activity, ensuring that the same security and compliance policies are enforced everywhere. This allows employees to safely use the tools that make them most effective, transforming shadow AI from a hidden risk into a governed productivity driver. For teams looking to build a robust AI governance program, this unified model offers a clear path forward.

Getting Started with Endpoint Governance

Managing the risks of shadow AI requires a strategy that balances security requirements with the need for employee productivity. Simply blocking tools is not a sustainable solution. Instead, organizations should focus on gaining visibility into AI usage and applying consistent governance policies from the cloud to the endpoint.

Teams evaluating solutions to address shadow AI risk can request a demo of Bifrost to see how its combined gateway and endpoint agent approach provides a comprehensive governance platform.

Sources

NIST, AI Risk Management Framework (AI RMF 1.0)
ManageEngine, 6 ways shadow AI is putting your organization at risk right now
Palo Alto Networks, What Is Shadow AI?
CIO, Shadow AI: The hidden risk expanding across the enterprise

Why Employees Use Unapproved AI Tools and How to Bring It Under Control

Katarina Hoffmann — Wed, 24 Jun 2026 17:25:03 +0000

[The rise of powerful, accessible AI tools has created a security blind spot known as shadow AI. This post explains how to regain control by extending AI governance to the endpoint with solutions like Bifrost and Bifrost Edge.]

The use of unapproved AI tools in the workplace, often called "shadow AI," has become a significant challenge for security and compliance teams. While IT departments work to deploy governed, secure AI platforms, employees frequently turn to public tools like ChatGPT, Claude, and various coding assistants for their daily tasks. A 2023 report by Cisco noted that while 80% of organizations have policies against using unapproved AI, many employees use them anyway, introducing risks of data leaks, compliance violations, and intellectual property loss.

This behavior is not driven by malicious intent but by a desire for efficiency. Employees adopt tools that help them work faster, and the friction of official channels can seem like a barrier. The solution is not to block all AI, but to implement a governance strategy that meets users where they are. Modern AI infrastructure, including tools like Bifrost, an open-source AI gateway from Maxim AI, can provide centralized control, but that control is incomplete if it doesn't reach the endpoint where these unapproved tools are used.

What is Shadow AI?

Shadow AI refers to any AI application or service used by employees without the organization's explicit approval or oversight. This includes using personal accounts for large language models (LLMs), installing desktop AI clients, or integrating ungoverned coding agents into an IDE. Much like "shadow IT" of the past decade, where employees used personal cloud storage or messaging apps for work, shadow AI arises from the gap between user needs and officially sanctioned tools.

The core problem is a lack of visibility and control. When an employee pastes sensitive internal data into a public web-based chatbot, the organization has no record of the event, no way to enforce its data protection policies, and no control over how that data might be used by the third-party service provider. According to a 2024 report from KPMG, 77% of executives are concerned about the security risks posed by generative AI.

The Risks of Ungoverned AI Usage

The use of unapproved AI tools introduces several categories of risk that can have serious consequences for an organization.

Key Risk Categories:

Data Exfiltration and IP Loss: Employees may inadvertently paste proprietary source code, customer data, financial reports, or strategic plans into public LLMs. This can lead to the organization's intellectual property being used to train third-party models or being exposed through a breach of the AI service itself.
Compliance Violations: For industries governed by regulations like HIPAA, GDPR, or SOC 2, using non-compliant AI tools to process sensitive data can result in severe penalties. Audit logs are a common requirement, and shadow AI usage leaves no trace.
Security Vulnerabilities: Many free AI tools and browser extensions have questionable security practices. They may lack robust access controls, operate with overly permissive data policies, or become vectors for malware.
Lack of Oversight: Without a central point of control, organizations cannot manage costs, enforce consistent behavior through guardrails, or monitor for appropriate use. There is no way to apply consistent role-based access control (RBAC) or budget limits.
Inaccurate or Hallucinated Outputs: Public models can produce incorrect information. When employees rely on these outputs for decision-making without proper verification, it can lead to poor business outcomes.

Why a Gateway Alone Is Not Enough

An AI gateway is a critical component of modern AI governance. It centralizes traffic, enforces access policies using virtual keys, manages costs, and provides observability. However, a gateway only governs the traffic that is explicitly configured to pass through it. It cannot control an employee who opens a browser and navigates directly to a public AI tool or installs a desktop AI application.

This creates a fundamental gap in enterprise AI strategy. Even with a robust gateway like Bifrost in place, the "last mile" to the user's machine remains unprotected. To solve the shadow AI problem, governance must be extended from the central infrastructure to the endpoint.

The Solution: AI Gateway + Endpoint Governance

A complete solution to shadow AI combines a central AI gateway as the policy and control plane with an endpoint agent that enforces those policies on every employee's machine. This "AI Gateway + Bifrost Edge" model provides comprehensive visibility and control without hindering productivity.

The Bifrost AI gateway serves as the brain of the operation. Here, administrators define all governance policies:

Virtual Keys and Access Profiles: Define which users, teams, or projects can access which models, with specific budgets and rate limits.
Guardrails: Configure rules to detect and redact sensitive data like secrets or PII before it leaves the corporate network using native secrets detection or integrations with services like AWS Bedrock Guardrails.
Audit Logs: Maintain a complete, immutable record of all AI interactions for compliance and security reviews.
Routing and Failover: Set up automatic fallbacks and load balancing to ensure reliability.

Bifrost Edge is the agent that extends these policies to every laptop and desktop. It runs on macOS, Windows, and Linux and transparently routes all AI traffic from supported applications through the organization's central Bifrost instance.

How Endpoint Governance Works

Discovery and Inventory: Bifrost Edge automatically detects all AI applications and Model Context Protocol (MCP) servers in use across the entire fleet of devices. This provides administrators with their first complete inventory of shadow AI usage.
Centralized App Management: From a central dashboard, administrators can review the discovered applications and explicitly approve or deny them. A denied application is blocked on the endpoint, preventing its use.
Transparent Routing: For approved apps, Edge routes their traffic through the Bifrost gateway. This is done without requiring any changes to the user's workflow or application configuration. The user continues to use their preferred tool, but all interactions are now fully governed.
Fleet-wide Deployment: Edge is designed for enterprise environments and can be deployed silently to thousands of machines using MDM solutions like Jamf, Microsoft Intune, or Kandji.

This combined approach closes the shadow AI gap. It allows employees to use powerful tools like Claude Desktop or ChatGPT while ensuring that every prompt and response is subject to the organization's security and compliance policies. It transforms unapproved AI tools from a risk into governed assets.

Taking Control of Your AI Landscape

Shadow AI is not a problem that can be solved with policy documents alone. It requires a technical solution that aligns security needs with user productivity. By combining a powerful AI gateway with endpoint governance, organizations can gain complete visibility and control over their entire AI ecosystem.

Teams looking to address the challenge of unapproved AI tools can evaluate solutions that provide this dual capability. Interested parties can request a Bifrost demo or review its capabilities in the open-source repository.

The Hidden Cost of Shadow AI: Data Exposure No One Is Logging

Katarina Hoffmann — Wed, 24 Jun 2026 17:22:57 +0000

The proliferation of generative AI tools has introduced unprecedented capabilities for productivity and innovation. However, it has also opened the door to significant security and compliance risks through what is commonly referred to as "shadow AI" – the use of AI applications and services within an organization without explicit IT approval or oversight. This uncontrolled usage creates a blind spot for security teams, leading to potential data exposure that often goes unlogged and unmitigated.

Understanding Shadow AI

Shadow AI emerges when employees leverage AI tools, often freely available or easily accessible, to perform tasks. This can include anything from using web-based AI assistants for drafting emails and code to integrating AI models directly into workflows via APIs or desktop applications. While these tools can enhance efficiency, their unmonitored use bypasses established security protocols, leaving sensitive corporate data vulnerable.

The core issue is that traditional security measures, which focus on network perimeters and explicitly approved applications, are often insufficient to capture or control AI traffic originating from employee endpoints. This uncontrolled access means that proprietary data, customer information, or intellectual property could be inadvertently shared with third-party AI models without any audit trail.

The Risks of Unlogged Data Exposure

When AI interactions are not logged, organizations lose visibility into where their sensitive data is going and how it might be used. This lack of oversight presents several critical risks:

Data Leakage: Employees might paste confidential information into public AI chat interfaces or allow AI agents to access sensitive codebases. Without logging, there's no way to detect or prevent these leaks.
Compliance Violations: Regulations like GDPR, HIPAA, and CCPA mandate strict data handling and privacy controls. Unlogged AI usage can lead to inadvertent data sharing that violates these regulations, resulting in hefty fines and reputational damage.
Intellectual Property Theft: Proprietary algorithms, trade secrets, or strategic plans shared with unmonitored AI tools could be exposed, potentially leading to competitive disadvantage or outright theft.
Security Vulnerabilities: AI agents or tools that connect to internal systems without proper authentication or authorization can introduce new attack vectors.

The challenge is that many modern AI tools, especially desktop applications and browser-based services, operate outside the typical network traffic monitoring that IT security teams rely on.

Bridging the Visibility Gap

Addressing shadow AI requires a multi-layered approach that extends governance to the endpoint where AI is actually being used. The Bifrost AI gateway, coupled with Bifrost Edge, offers a comprehensive solution to bring these unmonved AI interactions under control.

Bifrost, as an AI gateway, acts as the central policy engine. It provides a unified API for accessing various LLM providers, allowing organizations to configure governance policies, rate limits, virtual keys, and audit logging from a single control plane. However, a gateway only governs traffic that is explicitly routed through it.
This is where Bifrost Edge comes into play. As the endpoint governance layer, Bifrost Edge runs directly on employee machines (macOS, Windows, Linux) and automatically routes all AI traffic generated by supported applications and MCP servers through the organization's Bifrost gateway. This ensures that the same security policies, guardrails, and auditing capabilities configured at the gateway are enforced at the endpoint, regardless of whether the user intentionally configured their AI tool to use the gateway.

How Bifrost Edge Closes the Shadow AI Blind Spot

Endpoint-Level Routing: Bifrost Edge operates at the machine level, intercepting AI traffic from applications like Claude Desktop, ChatGPT, Cursor, and coding agents like Claude Code or Codex CLI, rerouting it through the approved Bifrost gateway. This eliminates the need for users to manually reconfigure application settings or swap SDKs.
Discovery and Governance of MCP Servers: Many AI applications can connect to external tools via Model Context Protocol (MCP) servers. Bifrost Edge inventories these MCP connections, providing administrators with visibility into which external tools are being used and allowing them to approve or deny them based on security policies.
Consistent Policy Enforcement: All governance policies, including virtual keys, budgets, rate limits, and guardrails (such as secrets detection and custom regex filtering), are managed centrally in Bifrost and enforced consistently by Bifrost Edge on each endpoint.
Fleet-Wide Deployment: Edge is designed for seamless deployment across an entire organization using existing Mobile Device Management (MDM) solutions like Jamf, Microsoft Intune, Kandji, and Workspace ONE. This ensures comprehensive coverage without requiring individual user intervention.

By extending governance directly to the endpoint, Bifrost Edge addresses the core problem of shadow AI: unlogged data exposure. It provides the visibility and control necessary to secure AI usage, ensure compliance, and protect intellectual property in the age of generative AI.

Sources

Bifrost AI Gateway Overview: https://docs.getbifrost.ai/overview
Bifrost Edge Overview: https://docs.getbifrost.ai/edge/overview
Bifrost MCP Governance: https://docs.getbifrost.ai/edge/mcp-governance
Bifrost App Governance: https://docs.getbifrost.ai/edge/app-governance
Bifrost Security & Guardrails: https://docs.getbifrost.ai/edge/security
Bifrost Deployment with MDM: https://docs.getbifrost.ai/edge/deployment-mdm
Bifrost Supported Applications: https://docs.getbifrost.ai/edge/supported-applications

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DEV Community: Katarina Hoffmann

10 Best AI Gateways for Python Teams

Key Criteria for Evaluating AI Gateways

The Top 10 AI Gateways

1. Bifrost

2. LiteLLM

3. Kong AI Gateway

4. Cloudflare AI Gateway

5. OpenRouter

6. Databricks AI Gateway (formerly MLflow AI Gateway)

7. Amazon Bedrock

8. Google Vertex AI Model Garden

9. NVIDIA NIM

10. Azure AI Studio

Conclusion: Making the Right Choice

Why 80% of Enterprise AI Projects Stall (and How to Avoid It)

The Hard Truth: High Failure Rates in Enterprise AI

Root Cause 1: Lack of Robust AI Governance and Security

Root Cause 2: Ineffective Evaluation and Quality Assurance

Root Cause 3: Deployment Complexity and Scalability Challenges

Root Cause 4: Misaligned Expectations and Unproven ROI

Strategies to Avoid Stalling: A Proactive Approach

Sources

Best Tools to Govern AI Across Managed and BYOD Devices

The Challenge of Endpoint AI Governance

Key Criteria for Evaluating AI Governance Solutions

Bifrost + Bifrost Edge: Comprehensive Endpoint AI Governance

Other Approaches and Solutions

1. General Mobile Device Management (MDM) Solutions

2. Endpoint Data Loss Prevention (DLP) Tools

3. Network Proxies and Firewalls

Implementing Comprehensive Endpoint AI Governance

Conclusion

Sources

A CISO's Playbook for Surfacing Every AI Tool Already in Use

The Challenge: Why Shadow AI Is Hard to Find

Stage 1: Initial Discovery and Baseline

Conduct User Surveys and Policy Reviews

Analyze Network and Proxy Logs

Stage 2: Automated Discovery with Endpoint Governance

How Endpoint Governance Works

From Discovery to a Fleet-Wide Inventory

Stage 3: Implementing Risk-Based Governance

Create an Approval Workflow

The "AI Gateway + Bifrost Edge" Model

Next Steps: Building a Sustainable Program

Sources

Blocking vs. Governing AI: Why the Difference Decides Your Security Posture

The Flawed Logic of Blocking AI

Understanding Shadow AI

The Governance Approach: See, Define, Enforce

Implementing AI Governance at the Endpoint

From Security Risk to Strategic Advantage

Sources

Reducing Shadow AI Risk Without Killing Employee Productivity

The Hidden Costs of Unmanaged AI Tools

Why Outright Bans on AI Tools Backfire

A Better Approach: Visibility and Governance at the Endpoint

How Bifrost Implements Endpoint AI Governance

The Control Plane: Bifrost AI Gateway

The Endpoint Agent: Bifrost Edge

The Benefits of Governed AI Adoption

Getting Started with Endpoint Governance

Sources

Why Employees Use Unapproved AI Tools and How to Bring It Under Control

What is Shadow AI?

The Risks of Ungoverned AI Usage

Key Risk Categories:

Why a Gateway Alone Is Not Enough

The Solution: AI Gateway + Endpoint Governance

How Endpoint Governance Works

Taking Control of Your AI Landscape

The Hidden Cost of Shadow AI: Data Exposure No One Is Logging

Understanding Shadow AI

The Risks of Unlogged Data Exposure

Bridging the Visibility Gap

How Bifrost Edge Closes the Shadow AI Blind Spot

Sources