Beyond Endpoint Protection: Alternatives to CrowdStrike and Forcepoint for Governing AI Usage

As organizations adopt generative AI, they face a new governance challenge that traditional endpoint security tools were not built to solve. This post examines the gap and explores how a dedicated AI gateway like Bifrost combined with an endpoint agent provides a more complete solution.

The rapid adoption of generative AI has created a significant governance blind spot for most organizations. Employees use tools like ChatGPT, Claude Desktop, and integrated coding agents daily, often without IT approval. This phenomenon, known as "shadow AI," extends the long-standing problem of shadow IT, but with higher stakes. Instead of just unsanctioned applications, employees now send sensitive intellectual property, customer data, and source code to third-party AI models.

Traditional endpoint security platforms like CrowdStrike and data security platforms like Forcepoint are adapting to this new reality. They provide essential protections against malware and data loss, but their architecture is fundamentally designed to secure devices and known data channels, not the nuanced, prompt-level interactions of AI. This creates a need for specialized alternatives built for the AI-native world. Bifrost, an open-source AI gateway, combined with its endpoint agent, Bifrost Edge, offers a purpose-built solution for this new class of risk.

How Traditional Security Platforms Approach AI Governance

Established leaders in endpoint and data security are extending their platforms to address AI. Their strengths lie in their existing footprint and deep capabilities in device and data-centric security.

CrowdStrike's Approach: Extending EDR to AI

CrowdStrike, a leader in endpoint detection and response (EDR), approaches AI governance by extending its Falcon platform to monitor AI-related activity on the endpoint. Its strategy focuses on visibility and threat detection.

Shadow AI Discovery: The Falcon platform can identify when employees use unauthorized AI applications, giving security teams visibility into which tools are running on company devices.
AI Detection and Response (AIDR): This capability inspects prompts for malicious content, stops data leaks, and enforces security policies at the interaction layer for specific AI applications.
Runtime Protection: The Falcon sensor provides visibility into the commands, file activity, and network connections of agentic applications, helping to detect behavior that deviates from the norm.

CrowdStrike's model is to treat AI applications as another process to be monitored, applying its powerful EDR and threat intelligence capabilities to detect and respond to threats.

Forcepoint's Approach: Data-Centric AI Security

Forcepoint focuses on data security, extending its Data Loss Prevention (DLP) and Cloud Access Security Broker (CASB) capabilities to AI. The core idea is to classify sensitive data and prevent it from entering AI tools, whether they are sanctioned or not.

Unified Data Security: Forcepoint aims to apply a single, unified policy framework across all channels, including cloud, web, email, and AI applications like Claude Enterprise and Microsoft 365 Copilot.
Data-Aware Governance: By integrating with AI platform APIs, Forcepoint can discover, tag, and act on regulated data whenever it is used, accessed, or generated within those platforms.
Shadow AI Discovery: Like CrowdStrike, Forcepoint provides visibility into unsanctioned AI usage and can enforce access controls through its web security gateway.

Forcepoint's strength is its data-first approach, ensuring that established data classification and protection policies extend to this new channel.

The Governance Gap: Why a Specialized Approach is Needed

While valuable, these traditional approaches leave a critical governance gap. They were not designed to manage the full lifecycle of an AI request, which requires more than just process monitoring or data pattern matching.

Key challenges include:

Lack of Granular Control: Blocking an application entirely is a blunt instrument. True governance requires controlling which models a user can access, setting spend budgets, and defining which tools an AI agent can execute.
Limited Context for Routing and Failover: Traditional security tools do not manage the connection to the LLM provider. They cannot automatically route traffic to a cheaper model, failover to a different provider during an outage, or load balance requests.
Inability to Govern Agentic Tools (MCP): Modern AI agents use protocols like the Model Context Protocol (MCP) to execute tools, read files, and interact with infrastructure. This creates a massive new surface area that network proxies and EDR agents cannot fully parse or control.

This is where a dedicated AI gateway provides a more complete and context-aware solution.

A Better Alternative: Bifrost AI Gateway + Bifrost Edge

A more effective architecture for AI governance combines a centralized policy engine with an endpoint agent that ensures all traffic flows through it. Bifrost and Bifrost Edge are designed precisely for this purpose. The Bifrost AI gateway acts as the control plane, while Bifrost Edge extends that control to every employee's machine.

Layer 1: The AI Gateway as a Central Control Plane

The Bifrost gateway is a high-performance, open-source service that sits between your applications and the AI providers. It gives platform teams a single point of control for all AI traffic.

Key gateway capabilities include:

Virtual Keys and Access Control: Instead of sharing raw provider API keys, administrators issue virtual keys. Each key can enforce granular policies, such as which models a user can access, spend limits, and rate limits. This allows teams to enable AI usage within safe, predefined boundaries.
Dynamic Routing and Failover: Bifrost can route requests between dozens of providers based on cost, performance, or availability. If a primary model provider has an outage, traffic can automatically failover to a backup.
MCP Governance: As an MCP gateway, Bifrost can inventory and filter the tools that AI agents are allowed to execute. This provides fine-grained control over the actions agents can take on behalf of users.
Audit and Observability: Every request, prompt, and response that passes through the gateway is logged, providing an immutable audit trail for compliance and security reviews.

Layer 2: Bifrost Edge for Endpoint Enforcement

A gateway only governs the traffic pointed at it. Bifrost Edge is a lightweight agent that runs on macOS, Windows, and Linux to solve this problem. It automatically and transparently routes all AI traffic from desktop apps, browser-based AI, and coding agents through the central Bifrost gateway.

This combination achieves what traditional tools cannot:

Complete Visibility: All shadow AI traffic becomes visible and manageable at the gateway.
Consistent Policy Enforcement: The same virtual keys, budgets, and guardrails configured in the gateway are automatically applied to every tool on every machine.
Zero User Friction: Developers and employees can continue using their preferred AI tools without changing configurations or workflows. Governance is applied invisibly in the background.

Making the Right Choice for AI Governance

For organizations just beginning to grapple with shadow AI, the visibility provided by CrowdStrike and Forcepoint is a critical first step. These platforms excel at what they were built for: securing endpoints and protecting data.

However, for teams that need to move beyond visibility to fine-grained control, cost management, and agent governance, a specialized solution is a better long-term fit. The combination of the Bifrost AI gateway and Bifrost Edge provides a purpose-built architecture for the unique challenges of enterprise AI. It turns governance from a reactive monitoring problem into a proactive control plane, enabling teams to say "yes" to AI innovation safely and securely.

Teams evaluating how to govern AI usage can request a demo of Bifrost to see how the gateway and endpoint agent work together.