---
description: Compare 9 AI data loss prevention tools on where they enforce, which AI traffic they see, and how they block or redact PII and secrets before data leaves.
title: 9 Best AI Data Loss Prevention Tools for LLM Traffic
image: https://storage.ghost.io/c/84/03/8403f2f6-141c-411a-8f55-a32d4291533e/content/images/size/w1200/2026/11/9-best-ai-data-loss-prevention-tools-for-llm-traffic-bifrost-1920x1080.png
---

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**TL;DR**

- AI data loss prevention (AI DLP) inspects prompts, uploads, model responses, and agent tool calls for sensitive data, then blocks, redacts, or logs it before it reaches a model provider.
- AI DLP tools enforce at four layers: the endpoint, the browser, the network (SSE proxy), and the AI gateway. Only the gateway layer also covers server-side applications and AI agents.
- Bifrost applies secrets detection, PII redaction, and custom regex guardrails to LLM calls and MCP tool calls, and Bifrost Edge extends the same policies to AI apps on employee devices.
- Network and endpoint DLP suites from Palo Alto Networks, Zscaler, Netskope, Microsoft, and Broadcom cover browser AI use but do not see API traffic from your own services.
- Most enterprises combine a gateway for application and agent traffic with endpoint or SSE controls for employee AI use.

One in five organizations reported a breach caused by shadow AI, according to [IBM's 2025 Cost of a Data Breach Report](https://newsroom.ibm.com/2025-07-30-ibm-report-13-of-organizations-reported-breaches-of-ai-models-or-applications,-97-of-which-reported-lacking-proper-ai-access-controls), which makes AI data loss prevention a standing requirement. [Bifrost](https://www.getmaxim.ai/), the [open-source AI gateway](https://github.com/maximhq/bifrost) built by Maxim AI, is the best choice for enterprises running mission-critical AI workloads that require top-tier performance, scalability, and reliability, and it enforces one DLP policy across model calls, MCP tool calls, and employee AI requests. This guide compares nine AI DLP tools by where they enforce, what traffic they see, and how they handle sensitive data.

## What Is AI Data Loss Prevention?

AI data loss prevention is the set of controls that detect and stop sensitive data, such as personal information, credentials, source code, and regulated records, from leaving an organization through prompts, uploads, model responses, and agent tool calls. AI DLP applies classic DLP detection to AI request and response bodies.

Traditional DLP was built for email, file shares, and USB drives. Generative AI adds three exfiltration paths:

- **Prompts and pasted text**, from employees or from applications that build prompts out of customer records.
- **Agent tool calls**, where an agent passes database rows or file contents to an MCP tool.
- **Model responses**, which can return sensitive values from retrieved documents or earlier turns.

The [OWASP Top 10 for LLM Applications](https://genai.owasp.org/llmrisk/llm022025-sensitive-information-disclosure/) lists sensitive information disclosure as LLM02 and recommends sanitization and pattern-based redaction before data is processed. AI DLP is one control inside the broader practice described in our guide to [AI guardrails and how they work](https://www.getmaxim.ai/articles/ai-guardrails-explained-what-they-are-and-how-they-work/).

## Types of AI DLP: Network, Browser, Gateway, and Endpoint

AI DLP tools differ mainly in where they sit between a person or application and the model. Endpoint agents see managed devices, browser controls see web AI apps, network and SSE proxies see decrypted web traffic, and an AI gateway sees every API call routed through it, including traffic from servers and agents.

![A prompt from an employee passes through endpoint, browser, network, and AI gateway control points before reaching a model provider, while server-side apps reach the gateway directly](https://articles-images-cdn.t3.tigrisfiles.io/diagrams/best-ai-data-loss-prevention-tools/best-ai-data-loss-prevention-tools-control-points.png)

*Figure 1: Endpoint, browser, and network DLP only see employee traffic; the AI gateway is the one control point that also covers server-side apps and agents.*

| DLP type | What it sees | Typical blind spot |
| --- | --- | --- |
| Endpoint DLP | Paste, upload, and app activity on managed devices | Server-side applications, unmanaged devices |
| Browser DLP | Prompts typed into web AI apps | Desktop apps, CLI coding agents, API traffic |
| Network / SSE DLP | Decrypted web traffic to AI domains | Certificate-pinned apps, traffic that skips the proxy |
| AI gateway DLP | Every LLM and MCP request routed through the gateway | Employee AI use that bypasses the gateway, unless extended to devices |

The gateway gap is the one teams miss most. Applications, RAG pipelines, and agents call model APIs from servers, so browser and endpoint controls never inspect that traffic. [PII redaction at the gateway](https://www.getmaxim.ai/articles/pii-redaction-at-the-gateway-before-data-reaches-providers/) is the only point where those prompts can be cleaned before they reach a provider.

## Key Criteria for Evaluating AI DLP Tools

The best AI DLP tool covers the traffic that carries your sensitive data, detects the data types you are regulated on, and remediates without breaking the application. Evaluate coverage first, then detection quality, then the redaction and logging behavior that decides whether security and engineering teams can live with it.

| Criterion | What to check | Why it matters |
| --- | --- | --- |
| Traffic coverage | Employee apps, browser AI, server apps, agents, MCP tool calls | Uncovered paths are where data leaves |
| Detection | PII types, secrets, custom patterns, semantic classifiers | Regex misses context; classifiers miss internal IDs |
| Remediation | Detect, block, redact, mask, coach | Redaction keeps work moving where blocking stops it |
| Response scanning | Outputs and tool results, not only prompts | Sensitive data also comes back from models and tools |
| Log hygiene | Redaction of stored logs and exported telemetry | Raw prompts in logs create a second copy of the data |
| Deployment | SaaS inspection vs self-hosted, in-VPC, or air-gapped | Regulated teams often cannot send prompts to a third party |

The [AI guardrails product page](https://www.getmaxim.ai/ai-guardrails) maps each criterion to the Bifrost controls that cover it.

## AI Data Loss Prevention Tools Compared at a Glance

The nine AI data loss prevention tools fall into four groups: an AI gateway that covers application, agent, and employee AI traffic, network and SSE platforms that extend web DLP to generative AI, endpoint and information protection suites, and cloud PII services tied to one cloud.

| Tool | Enforcement layer | AI traffic covered | Actions on sensitive data | Deployment |
| --- | --- | --- | --- | --- |
| Bifrost | AI gateway, plus endpoint with Bifrost Edge | LLM calls, MCP tool calls, employee desktop, CLI, and browser AI | Detect, block, redact (replace, mask, hash), reversible log placeholders | Self-hosted, in-VPC, air-gapped |
| Palo Alto Networks AI Access Security | Network, SSE, secure browser | Employee use of GenAI apps | Block in prompts and uploads; allow, block, or limit apps | NGFW, Prisma Access, Prisma Browser |
| Zscaler Generative AI Security | SSE proxy, browser isolation | Employee use of AI apps | Block sensitive prompts, isolate apps | Cloud (SSE) |
| Netskope GenAI App Security | SSE proxy, endpoint | Employee use of AI apps by account instance | Redact, block, coach | Cloud (SSE) |
| Cloudflare AI Gateway DLP | AI gateway (managed) | API traffic routed through Cloudflare | Block or flag prompts and responses | Cloudflare network |
| Microsoft Purview | Endpoint, browser, Microsoft 365 | Copilot and third-party AI sites on onboarded Windows devices | Warn, block, honor sensitivity labels | Microsoft cloud, endpoint agent |
| Broadcom Symantec DLP | Endpoint, network, cloud | Employee prompts to third-party AI tools | Detect, block unsanctioned apps | On-prem and cloud |
| Google Cloud Sensitive Data Protection | API service | Prompts, responses, training data on Google Cloud | Inspect, redact, replace, encrypt | Google Cloud |
| AWS Bedrock Guardrails | Model guardrail service | Prompts and responses on Amazon Bedrock | Block, mask, detect | AWS |

The Bifrost row reflects the open-source gateway plus [Bifrost Enterprise](https://docs.getbifrost.ai/enterprise/overview), which adds guardrails and Edge support.

## 1. Bifrost: Gateway-Level DLP for LLM, MCP, and Employee AI Traffic

[Bifrost](https://www.getmaxim.ai/) enforces AI data loss prevention in the request path, before a prompt reaches any of its [10,000+ models across 25+ providers](https://docs.getbifrost.ai/providers/supported-providers/overview) and before an MCP tool receives its arguments. Guardrail rules decide which traffic is checked, guardrail profiles decide how, and each match is detected, blocked, or redacted.

**Best for:** Bifrost is built for enterprises running mission-critical AI workloads that require top-tier performance, scalability, and reliability. It serves as a centralized AI gateway to route, govern, and secure all AI traffic across models and environments with ultra low latency. Bifrost unifies LLM gateway, MCP gateway, and Agents gateway capabilities into a single platform. Designed for regulated industries and strict enterprise requirements, it supports air-gapped deployments, VPC isolation, and on-prem infrastructure. It provides full control over data, access, and execution, along with strong security, policy enforcement, and governance capabilities.

![A prompt or MCP tool call is matched to a virtual key and a CEL rule, checked by guardrail profiles, then forwarded as is, forwarded redacted, or blocked](https://articles-images-cdn.t3.tigrisfiles.io/diagrams/best-ai-data-loss-prevention-tools/best-ai-data-loss-prevention-tools-guardrail-decision.png)

*Figure 2: The same rule and profile decide whether sensitive content is passed, redacted, or blocked, for model calls and MCP tool calls alike.*

In [Bifrost guardrails](https://docs.getbifrost.ai/enterprise/guardrails), rules are CEL expressions that select requests by [virtual key](https://docs.getbifrost.ai/features/governance/virtual-keys), team, user, model, or MCP tool, and profiles configure the detection engine. As Figure 2 shows, each request gets one decision on the input phase and another on the output phase.

- [**Secrets detection**](https://docs.getbifrost.ai/enterprise/guardrails/secrets-detection) uses the 222 Gitleaks v8.30.1 default rules to catch API keys, tokens, and private keys. Our guide to [secrets detection in LLM prompts](https://www.getmaxim.ai/articles/secrets-detection-llm-prompts/) covers tuning.
- [**Custom regex**](https://docs.getbifrost.ai/enterprise/guardrails/custom-regex) handles internal identifiers and includes a PII template for emails, US phone numbers, US Social Security numbers, credit-card-like numbers, and IP addresses. Names need a semantic engine.
- **External PII engines** plug in as profiles: Bifrost supports 13 external providers, including [Microsoft Presidio](https://docs.getbifrost.ai/integrations/guardrails/presidio), Azure AI Language PII, AWS Bedrock Guardrails, Google Model Armor, and CrowdStrike AIDR.
- **MCP coverage**: rules with the `mcp` target redact tool arguments before execution and tool results before they return.

### Redaction that also cleans the logs

Bifrost redacts by replace, mask, or hash, in three [redaction modes](https://docs.getbifrost.ai/enterprise/guardrails/redaction): runtime, logs-only, and reversible, which writes placeholders such as `[EMAIL-1]` that only users with the `Logs:Reveal` permission can resolve. [Content logging](https://docs.getbifrost.ai/features/observability/content-logging) can be disabled per virtual key or project.

### Extending gateway DLP to employee devices

[Bifrost Edge](https://www.getmaxim.ai/edge) routes AI traffic from Claude Desktop, ChatGPT, Claude Code, Codex CLI, and browser AI on [chatgpt.com](http://chatgpt.com/) and [claude.ai](http://claude.ai/) through the Bifrost AI gateway, so [the same guardrail profiles apply](https://docs.getbifrost.ai/edge/security) before data leaves the device. Edge requires Bifrost Enterprise, routes only AI traffic, and runs alongside existing endpoint security tools.

Bifrost runs self-hosted, in-VPC, or air-gapped, and no prompts or responses leave the customer's infrastructure, a requirement for many [enterprise deployments](https://www.getmaxim.ai/enterprise). The [guardrails resource hub](https://www.getmaxim.ai/resources/guardrails) collects configuration patterns.

## Network and SSE DLP Tools for Generative AI

Network and security service edge (SSE) platforms extend existing web DLP policies to generative AI apps. They inspect decrypted traffic between employees and AI websites, classify the apps in use, and block or redact sensitive data in prompts and uploads. They are strongest for browser-based AI use on managed networks.

Server applications that call model APIs directly sit outside their path, so most teams pair them with gateway-level [AI security controls](https://www.getmaxim.ai/resources/ai-security).

### 2. Palo Alto Networks AI Access Security

AI Access Security discovers GenAI apps from a catalog of more than 500, each scored on 60+ risk attributes, and uses Enterprise DLP to block PII, source code, and intellectual property in prompts and file uploads. Administrators can allow, block, or limit apps, app functions, browser extensions, and custom GPTs by user group. It runs on NGFW, Prisma Access, and Prisma Browser.

**Best for:** Organizations standardized on Prisma Access or Palo Alto firewalls.

### 3. Zscaler Generative AI Security

Zscaler applies prompt-level visibility and DLP to block sensitive data in prompts, with dashboards showing which AI apps each department uses. Browser Isolation renders AI apps in an isolated browser with clipboard uploads and downloads restricted.

**Best for:** Zscaler customers that want to isolate AI apps for high-risk user groups.

### 4. Netskope GenAI App Security

Netskope inspects traffic to more than 1,800 AI apps and separates corporate accounts from personal accounts on the same app. When DLP matches, it can redact the detail and let the task continue, and real-time coaching points users to approved tools.

**Best for:** Teams that want one DLP classification standard across email, SaaS, endpoints, and AI prompts.

### 5. Cloudflare AI Gateway DLP

Cloudflare AI Gateway scans prompts and responses with Cloudflare's DLP detection profiles. A block action returns a DLP error; a flag action returns the response and records findings in a header and logs. Response scanning buffers streaming responses, which raises time to first token, and the feature is labeled beta in Cloudflare's documentation.

**Best for:** Teams already routing model API traffic through Cloudflare.

## Endpoint and Information Protection DLP for AI

Endpoint and information protection suites apply DLP on managed devices and inside productivity platforms. They reuse the classification and labeling programs enterprises already run and extend them to AI assistants and AI websites. Coverage depends on device enrollment and on which applications the suite integrates with.

Terminal coding agents and desktop AI apps are a recurring gap here, a pattern covered in our analysis of [endpoint AI governance where employees use AI](https://www.getmaxim.ai/articles/endpoint-ai-governance-controlling-ai-where-employees-actually-use-it/).

### 6. Microsoft Purview

On Windows devices onboarded to Purview, endpoint DLP can warn or block users who paste sensitive information, such as credit card numbers, into third-party AI sites in the browser. Microsoft 365 Copilot honors sensitivity labels, and DSPM for AI reports sensitive data found in prompts and responses.

**Best for:** Microsoft 365 organizations that want Copilot and browser AI under existing labels.

### 7. Broadcom Symantec DLP

Symantec DLP detects what employees enter into third-party AI tools such as ChatGPT and NotebookLM, discovers AI apps in use, and blocks unsanctioned ones. For Copilot, it supports the attribute that keeps labeled documents out of Copilot processing.

**Best for:** Enterprises with an established Symantec DLP policy library.

## Cloud PII Redaction Services for Model Traffic

Cloud PII services inspect and de-identify sensitive data inside one provider's AI stack. They offer strong classifiers and fine-grained redaction for that cloud's models, and a multi-provider gateway can call them as one step in a broader policy.

### 8. Google Cloud Sensitive Data Protection

Sensitive Data Protection detects more than 150 built-in infoTypes, including names and financial identifiers, and de-identifies matches by redaction, replacement, or format-preserving encryption across prompts, responses, and Vertex AI training data. Bifrost applies these templates to any provider through its [Google Model Armor integration](https://www.getmaxim.ai/bifrost/blog/redacting-sensitive-data-in-llm-traffic-with-google-model-armor-and-bifrost).

**Best for:** Google Cloud teams that need semantic PII detection.

### 9. AWS Bedrock Guardrails

Bedrock Guardrails sensitive information filters use a machine learning model to find PII in prompts and responses, including code. Each type can be blocked, masked to a placeholder like `{EMAIL}`, or detected, with separate input and output actions and custom regex. Masking does not cover tool-use fields or invocation logs. See the setup for [Bedrock Guardrails PII detection in Bifrost](https://www.getmaxim.ai/articles/aws-bedrock-guardrails-in-bifrost-pii-detection-and-content-filtering-setup/).

**Best for:** AWS teams running models on Amazon Bedrock.

## How to Choose an AI Data Loss Prevention Tool

Choose an AI data loss prevention tool by mapping the traffic that carries sensitive data to the layer that can see it. Application and agent traffic needs a gateway, employee AI tools on managed devices need endpoint coverage, and unmanaged browsers need SSE or browser controls. Most enterprises need two of the three.

![Decision flow that sends application and agent traffic to an AI gateway, employee AI tools to endpoint coverage, and unmanaged browser use to SSE or browser DLP](https://articles-images-cdn.t3.tigrisfiles.io/diagrams/best-ai-data-loss-prevention-tools/best-ai-data-loss-prevention-tools-selection-flow.png)

*Figure 3: Start with the traffic you need to cover, then pick the layer that can see it; most enterprises need more than one.*

- **Applications, RAG pipelines, or agents send customer data to models:** only an AI gateway sees that traffic. Bifrost covers every provider and MCP tool calls.
- **Employees use desktop AI, browser AI, or coding agents on company devices:** use endpoint coverage, either Bifrost Edge with gateway guardrails or an endpoint DLP suite.
- **Contractors or BYOD users reach AI from unmanaged browsers:** an SSE proxy or secure browser is the practical control.

Regulated teams that keep customer data in-region often rule out SaaS inspection of prompts, which favors self-hosted gateway enforcement; the [financial services page](https://www.getmaxim.ai/industry-pages/financial-services-and-banking) covers those requirements.

## AI DLP Best Practices for Enterprise Teams

Effective AI DLP programs define policy once, enforce it at every layer that carries AI traffic, inspect responses and tool results as well as prompts, and keep logs free of the data they protect. These practices reflect how gateway-level DLP runs across production LLM and MCP traffic.

![Desktop apps, coding agents, and browser AI route through Bifrost Edge to the Bifrost AI gateway, which also serves server apps and forwards to providers and MCP servers](https://articles-images-cdn.t3.tigrisfiles.io/diagrams/best-ai-data-loss-prevention-tools/best-ai-data-loss-prevention-tools-gateway-and-edge.png)

*Figure 4: Guardrails are configured once in the gateway; Bifrost Edge brings employee AI traffic under the same rules.*

- **Keep one policy source.** As Figure 4 shows, [the Bifrost AI gateway](https://www.getmaxim.ai/) holds the guardrail rules, and [Bifrost Edge](https://docs.getbifrost.ai/edge/overview) enforces them on devices.
- **Redact before you block.** Reversible placeholders keep requests useful and preserve an investigation trail.
- **Scan outputs and tool results** with output-phase rules on the same profiles.
- **Clean the logs** with logs-only or reversible redaction, and disable content logging for the most sensitive keys.
- **Close the shadow AI gap.** See our breakdown of [seven shadow AI exposure categories](https://www.getmaxim.ai/articles/shadow-ai-risks-7-exposure-categories-and-the-control-for-each-2026/) and the [shadow AI resource page](https://www.getmaxim.ai/resources/shadow-ai).
- **Audit policy changes** with HMAC-signed [audit logs](https://docs.getbifrost.ai/enterprise/audit-logs) of administrative activity.

AI DLP is one layer of a wider program that also covers prompt injection and content safety, as outlined in our [explainer on AI guardrails](https://www.getmaxim.ai/articles/ai-guardrails-explained-what-they-are-and-how-they-work/) and our guide to [governing AI in the browser](https://www.getmaxim.ai/articles/governing-ai-in-the-browser-chatgpt-claude-ai-and-beyond/).

## Frequently Asked Questions

### What is AI DLP?

AI DLP, or AI data loss prevention, detects and stops sensitive data from leaving an organization through AI prompts, uploads, model responses, and agent tool calls. It applies detection for PII, credentials, source code, and regulated records, then blocks, redacts, or logs matches before data reaches a model provider or external tool.

### Can DLP tools detect sensitive data in ChatGPT prompts?

Yes, when the tool sits on the prompt's path. Endpoint, browser, and SSE DLP tools inspect ChatGPT prompts on managed devices or through a corporate proxy. Bifrost Edge routes ChatGPT web and desktop traffic through the Bifrost AI gateway, so the guardrails that cover API traffic also cover those prompts.

### What is the difference between endpoint DLP and network DLP for AI?

Endpoint DLP runs on the device and sees paste, upload, and app activity on any network. Network DLP runs in a proxy and sees decrypted web traffic that crosses it. Neither sees server-side API calls from applications or agents, which is why an AI gateway is the third layer in most AI DLP programs.

### How does PII redaction work for LLM traffic?

PII redaction detects personal data in a prompt or response and replaces it before the content moves on. Bifrost can replace, mask, or hash matches, and its [reversible redaction mode](https://docs.getbifrost.ai/enterprise/guardrails/redaction) writes numbered placeholders to logs so authorized reviewers can recover originals while providers only see the placeholder.

### Can AI DLP stop API keys and secrets from leaking into prompts?

Yes. Bifrost Enterprise includes a secrets detection guardrail based on the Gitleaks default rules that scans prompts and responses for API keys, tokens, and private keys, then blocks or redacts them. This matters most for coding agents, which often read configuration files and environment variables into their context.

### What causes AI data leakage?

AI data leakage usually comes from employees pasting sensitive content into unapproved AI tools, applications building prompts from customer records, agents passing data to external tools, and logs that store raw prompts. IBM's 2025 report found that one in five organizations had a breach tied to shadow AI.

## Get Started with Bifrost for AI Data Loss Prevention

AI data loss prevention works when it covers every path sensitive data takes to a model: applications, agents, MCP tools, and the AI tools employees use daily. Bifrost enforces secrets detection, PII redaction, and custom patterns at the gateway, keeps logs clean, and extends the same policies to devices with Bifrost Edge. [Request Bifrost Edge](https://www.getmaxim.ai/edge/request-access) for device coverage, or [book a demo](https://www.getmaxim.ai/book-a-demo) with the Bifrost team to map your AI DLP requirements to a deployment.

## Read next

[![9 Best AWS Bedrock Alternatives](https://storage.ghost.io/c/84/03/8403f2f6-141c-411a-8f55-a32d4291533e/content/images/size/w720/2026/11/9-best-aws-bedrock-alternatives-bifrost-1920x1080.png) AWS Bedrock alternatives are model platforms that serve foundation models outside Amazon's managed service. This guide compares nine of them, including Google Vertex AI, Microsoft Foundry, Together AI, and vLLM, and shows how to run several behind one API.](https://www.getmaxim.ai/articles/9-best-aws-bedrock-alternatives/)

[![13 Best AI Coding Agents for Enterprise Teams](https://storage.ghost.io/c/84/03/8403f2f6-141c-411a-8f55-a32d4291533e/content/images/size/w720/2026/11/13-best-ai-coding-agents-for-enterprise-teams-bifrost-1920x1080.png) AI coding agents are tools that plan, edit, and test code across a repository from a single instruction. This guide compares 13 of them, including Claude Code, GitHub Copilot, Cursor, and Kiro, on the controls enterprise teams check before a rollout.](https://www.getmaxim.ai/articles/13-best-ai-coding-agents-for-enterprise-teams/)

[![11 Best LLM Proxies: Open Source and Managed](https://storage.ghost.io/c/84/03/8403f2f6-141c-411a-8f55-a32d4291533e/content/images/size/w720/2026/11/11-best-llm-proxies-open-source-and-managed-bifrost-1920x1080.png) An LLM proxy sits between applications and model providers and forwards their requests. This guide compares 11 open-source and managed options, including Bifrost, Cloudflare AI Gateway, Kong AI Gateway, and Azure API Management, on routing, failover, caching, and governance.](https://www.getmaxim.ai/articles/11-best-llm-proxies-open-source-and-managed/)

## More in AI Gateway

[![13 Best OpenAI API Alternatives](https://storage.ghost.io/c/84/03/8403f2f6-141c-411a-8f55-a32d4291533e/content/images/size/w720/2026/11/13-best-openai-api-alternatives-bifrost-1920x1080.png) OpenAI API alternatives are model providers you can call instead of, or alongside, OpenAI. This guide compares 13 of them, including Anthropic, Google Gemini, Groq, and Amazon Bedrock, on SDK compatibility, pricing model, rate limits, and fit.](https://www.getmaxim.ai/articles/13-best-openai-api-alternatives/)

[![9 Best Cursor Alternatives for Teams](https://storage.ghost.io/c/84/03/8403f2f6-141c-411a-8f55-a32d4291533e/content/images/size/w720/2026/11/9-best-cursor-alternatives-for-teams-bifrost-1920x1080.png) Cursor alternatives are AI code editors and coding agents that teams adopt in place of Cursor or beside it. This guide compares nine of them, including GitHub Copilot, Claude Code, JetBrains AI, and Zed, on identity, admin controls, model access, and endpoint support.](https://www.getmaxim.ai/articles/9-best-cursor-alternatives-for-teams/)

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