Best Tools to Monitor ChatGPT and Claude Usage at Work

The rapid adoption of generative AI tools like ChatGPT and Claude introduces significant visibility and governance challenges for enterprises. This article examines why monitoring AI usage is crucial and compares leading solutions, including Bifrost, for effective workplace AI governance.

The emergence of powerful large language models (LLMs) such as ChatGPT and Claude has transformed workplace productivity. Employees readily integrate these tools into daily workflows for everything from drafting emails to generating code. While beneficial, this widespread adoption often outpaces organizational governance, creating what is known as "shadow AI." This unmanaged usage can lead to critical data leakage, compliance violations, and a significant lack of visibility into how company information interacts with external AI services. Organizations require dedicated tools to monitor and control the use of these generative AI applications on corporate networks and devices.

The Challenge of Shadow AI in the Workplace

Shadow AI refers to the use of unauthorized or unmanaged AI tools, models, and workflows that operate outside official governance, security, or compliance controls. Employees often turn to unsanctioned AI tools to work faster and solve problems, but this introduces severe unintended consequences. Sensitive information, such as proprietary code, customer personally identifiable information (PII), or financial data, can inadvertently be exposed to external AI services without oversight. Samsung, for example, experienced incidents where employees unintentionally uploaded sensitive source code to ChatGPT, highlighting the real-world risks.

Traditional data loss prevention (DLP) solutions, designed for static files and email attachments, often struggle to monitor data in motion within conversational AI interfaces. Prompts and AI-generated outputs are unstructured, real-time, and continuously transformed, rendering legacy DLP ineffective in many generative AI contexts. Without dedicated tools, IT and security teams lack visibility into which AI tools employees are using, what data is being shared, and whether that usage aligns with internal policies and regulatory requirements like GDPR or HIPAA.

Key Capabilities for Effective AI Usage Monitoring

Effective monitoring and governance of generative AI usage in the workplace require a multifaceted approach that goes beyond simple network blocking. Modern AI governance platforms offer capabilities specifically designed to address the unique challenges posed by LLMs:

• Visibility and Discovery: The foundational step is to identify all AI tools and services in use across the organization, including desktop applications, browser extensions, coding assistants, and API calls. This includes both sanctioned enterprise AI deployments and unsanctioned shadow AI.
• Content Inspection and Data Loss Prevention (DLP): Solutions must be able to inspect prompts and responses in real-time for sensitive data, such as PII, financial information, or intellectual property. This allows for the blocking or redaction of sensitive content before it leaves the corporate environment.
• Policy Enforcement and Guardrails: Organizations need to define and enforce rules around which AI tools are permitted, how they can be used, and what data they can access. This includes setting usage policies, budgets, and safety guardrails that apply consistently across various AI interactions.
• Endpoint-level Monitoring: Many AI interactions occur directly on employee devices through desktop applications or browser interfaces. Robust solutions offer endpoint agents to capture and govern these interactions, providing visibility into user actions like copy/paste and file uploads to AI tools.
• Audit Trails and Analytics: Comprehensive logging of AI activity, including user, device, tool, model, and data interactions, is essential for compliance, incident forensics, and optimizing AI adoption.
• Integration with Enterprise IT: Seamless integration with existing identity management (SSO), device management (MDM), and security information and event management (SIEM) systems simplifies deployment and management across the enterprise.

Top Tools for AI Usage Monitoring and Governance

Several platforms are emerging to help organizations gain control over generative AI usage.

Bifrost Edge

For enterprises seeking comprehensive endpoint AI governance and compliance, Bifrost Edge stands out as an integrated solution that extends an AI gateway's policy enforcement directly to employee machines. Bifrost, an open-source AI gateway from Maxim AI, serves as the central control plane, where virtual keys, budgets, rate limits, and guardrails are configured. Bifrost Edge then deploys to every macOS, Windows, and Linux device, carrying those same governance policies to the endpoint. [cite: docs.getbifrost.ai/edge/overview]

The solution transparently routes all AI traffic—from desktop applications like Claude Desktop and Cursor to browser-based AI like ChatGPT web and coding agents such as Claude Code—through the organization's Bifrost gateway. This eliminates "shadow AI" by bringing ungoverned usage under central policy without requiring users to manually reconfigure their applications. [cite: docs.getbifrost.ai/edge/how-it-works, docs.getbifrost.ai/edge/supported-applications]

Bifrost Edge provides app governance, allowing administrators to specify which AI applications are permitted, blocking disallowed tools before any data leaves the machine. It also offers Model Context Protocol (MCP) governance, inventorying and controlling the MCP servers configured within AI apps to prevent sensitive data exposure through autonomous agents. [cite: docs.getbifrost.ai/edge/app-governance, docs.getbifrost.ai/edge/mcp-governance] Security guardrails configured within the Bifrost gateway, such as native secrets detection and custom regex patterns, are automatically enforced on endpoint AI traffic, protecting prompts and responses from sensitive content leakage. [cite: docs.getbifrost.ai/edge/security]

Deployment is designed for fleet-wide rollout via existing Mobile Device Management (MDM) platforms like Jamf, Microsoft Intune, and Kandji, with a managed configuration that points agents to the organization's Bifrost. [cite: docs.getbifrost.ai/edge/deployment-mdm] While currently in alpha, Bifrost Edge positions itself as a robust solution for organizations needing to enforce AI policies directly on employee devices.

Best for: Enterprises needing comprehensive, transparent endpoint AI governance and compliance, especially those with diverse AI tool usage across employee machines.

Other Notable Tools

• Teramind: This platform offers employee activity monitoring with specific features for tracking ChatGPT usage. It provides real-time alerts, screen recordings, and DLP capabilities to prevent data exfiltration through AI tools, offering deep visibility into user interactions and forensic evidence.
  • Best for: Detailed employee monitoring and insider threat detection for AI usage.
• BigID: Specializing in data discovery and governance, BigID helps identify shadow AI tools and connect their activity to sensitive data, identities, and access paths across the enterprise. It assists in prioritizing risks and automating remediation workflows.
  • Best for: Organizations focused on identifying and governing sensitive data exposure via shadow AI.
• Cloudflare AI Gateway: As a network-level AI gateway, Cloudflare AI Gateway sits between applications and AI providers to offer observability, rate limiting, caching, and DLP features. It provides centralized visibility and control over AI applications by connecting them with a single line of code.
  • Best for: Teams needing network-edge control, performance optimization, and basic governance for AI traffic.
• Zenity: This AI security and governance platform focuses on discovering, monitoring, and controlling AI agents across SaaS, cloud, custom stacks, and endpoints. Zenity aims to provide a unified view of every AI agent in the environment, emphasizing AI security.
  • Best for: Security teams requiring a unified view and control over AI agents, particularly for AI security postures.
• Netwrix 1Secure: Netwrix 1Secure approaches AI governance from the data access layer. It controls what sensitive data AI agents and tools can reach across hybrid environments and incorporates endpoint DLP to intercept and block sensitive data from being submitted into external LLMs.
  • Best for: Enterprises needing to govern sensitive data access by AI tools and enforce endpoint DLP for LLM interactions.

Implementing a Comprehensive AI Governance Strategy

Establishing effective AI governance involves more than just selecting a tool; it requires a strategic approach. Organizations should start by understanding their existing AI footprint, both sanctioned and shadow, and define clear policies for acceptable use. Technical controls, such as those offered by an AI gateway combined with endpoint agents, are essential for enforcing these policies. An AI gateway centralizes governance, applies guardrails, and routes traffic efficiently, while endpoint solutions extend these controls to the individual device, capturing the full spectrum of AI interactions.

This layered approach helps organizations mitigate risks like data leakage and compliance breaches while still enabling employees to harness the productivity benefits of generative AI. Regular auditing and continuous monitoring ensure that policies remain effective as AI technology and usage patterns evolve.

Teams evaluating AI usage monitoring can request a Bifrost demo or review the open-source repository to understand how the platform provides comprehensive visibility and control over generative AI deployments.

Sources

• Teramind. "Monitor Employee Activity on ChatGPT To Prevent Data Leaks." Teramind Blog, April 28, 2026.
• BigID. "Shadow AI Discovery & Governance for Enterprise AI Risk." BigID.com, accessed July 8, 2026.
• Cyberhaven. "Generative AI DLP: Protect Sensitive Data in AI Tools." Cyberhaven Blog, May 25, 2026.
• Cloudflare. "AI Gateway | Observability for AI applications." Cloudflare.com, accessed July 8, 2026.
• DynamicsSmartz. "What is Shadow AI? Risks, Governance, and How to Control It." DynamicsSmartz Blog, March 3, 2026.