ChatGPT SearchOct 12, 2026
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Data as of Oct 12, 2026A topic in Data Loss Prevention Software.
Reviewed by Dimitry Apollonsky
AI summary
Nightfall holds a clear lead for stopping sensitive data leaks into generative AI tools using browser extensions. LayerX Security is the usual choice for stopping employees from pasting proprietary code into public chatbots.
browser extensions that redact and block proprietary code, API keys, and sensitive prompts
securing cloud interactions and stopping data loss across generative AI applications
tracking sensitive data movement and lineage as it flows into external tools
agentless real-time redaction and blocking of sensitive data pasted into AI chatbots
browser extension monitoring to prevent copy-pasting code into web-based AI tools
Mentioned in · Jul 7 – Oct 12, 2026
ChatGPT SearchOct 12, 2026
If your employees can paste proprietary source code into public generative AI chatbots without detection, you’re looking for browser-based, AI-aware data loss prevention (DLP) — security controls that inspect what employees submit to tools such as ChatGPT, Claude, and Gemini, and block or flag sensitive content before it leaves your organization.
The key distinction is between traditional DLP, which may focus on files, email, and network traffic, and browser-level controls that can inspect prompts, pasted code, and other inputs to generative AI applications.
These are the most directly relevant products I found, ranging from dedicated browser extensions to enterprise browser-security platforms.
1. Containment — AI Chat Firewall
(Direct match)
Its browser extension checks submissions to ChatGPT, Claude, Gemini, Copilot, and Grok against organizational policies before they reach the provider. Explicitly identifies proprietary source-code leakage as a use case, with configurable blocking.
Website:
2. Prisma Browser GenAI Prompt Controls
An enterprise option for organizations wanting centralized policy enforcement. It supports controls for ChatGPT, Claude, Gemini, Copilot, Perplexity, and embedded AI widgets, including rules to block prompts matching DLP profiles for proprietary content.
Documentation:
3. STYX by heLLM
A browser-native DLP product that scans prompts, pastes, and uploads in ChatGPT, Claude, Gemini, and other AI tools. Offers block, warn, redact, and log actions, with custom rules and managed browser deployment.
Website:
4. TeamPrompt
A browser extension for Chrome, Firefox, and Edge that scans messages for sensitive information before submission. Supports custom patterns and managed deployment through enterprise device-management tools.
Website:
5. Entrovik Web AI Guard
Worth evaluating if your main concern is source code and technical files. It describes inspection of prompts and supported text attachments, including source-code, JSON, XML, YAML, CSV, and configuration files, before submission to supported AI websites.
Website:
| Requirement | What to verify in a vendor demo |
|---|---|
| Proprietary source-code detection | Can it detect pasted source code, not just API keys and credit-card numbers? |
| Blocking before transmission | Is the prompt stopped locally before it reaches the AI provider? |
| Custom policies | Can you define rules for internal code patterns, confidential project identifiers, and secret repositories? |
| Broad browser coverage | Does it cover ChatGPT, Claude, Gemini, Copilot, and other tools your staff use? |
| Enterprise management | Can IT deploy the extension centrally, prevent tampering, and audit violations? |
My starting shortlist would be Containment and Prisma Browser for a security-team-led evaluation, with STYX as a focused browser-native alternative. Entrovik is also worth a closer look if inspection of source-code attachments is important.
One important caveat: detecting known secrets and custom patterns is not the same as reliably identifying arbitrary proprietary source code. Ask vendors to demonstrate detection against representative code snippets from your own environment, including code without credentials or obvious confidential labels.
The goal is to test the actual enforcement point: an employee pastes confidential code into a public AI chat, clicks Send, and the content is blocked before it leaves the browser.
Google AI ModeSep 23, 2026
Detecting and blocking code pastes into public chatbots requires intercepting actions at the client or browser layer, because traditional network proxies and CASBs often struggle to inspect encrypted web traffic or client-side JavaScript interactions within modern web apps.[](https://nhimg.org/faq/how-should-security-teams-stop-sensitive-data-from-being-pasted-into-genai-tools/)
Several modern security vendors specialize in browser-based or browser-integrated Data Loss Prevention (DLP) tailored for Generative AI:
Our team is pasting proprietary code into public AI tools, and we need a way to stop it. What browser-based DLP solutions are designed for generative AI?
Google AI ModeOct 4, 2026
Protecting proprietary data from leaking into public Large Language Models (LLMs) requires specialized Generative AI Data Loss Prevention (DLP) and Security Access Service Edge (SASE) tools . These platforms intercept prompts, code, and file uploads before they leave your corporate network or endpoint to reach public tools like ChatGPT, Claude, or Gemini.[](https://dope.security/post/top-ai-dlp-tools-chatgpt-claude-2026)
Top providers offering GenAI DLP capabilities include:
ChatGPT SearchOct 4, 2026
| Privacy | Does detection happen locally, and what prompt content is retained in logs? |
If you'd like to narrow this down, let me know:
If you'd like to narrow this down, let me know:
Yes. There is now a distinct “GenAI DLP” / “AI data security” category aimed specifically at detecting and preventing proprietary information from being pasted, uploaded, or otherwise sent to public LLMs.
| Vendor | What it does | Best fit |
|---|---|---|
| Nightfall AI | Inspects prompts, uploads, clipboard data, etc.; detects PII, secrets, proprietary data and can block/redact. Explicitly targets ChatGPT and other AI apps. | AI-native DLP / fastest focused solution |
| Forcepoint | AI Data Security inspects prompts, responses and file uploads across ChatGPT, Claude, Copilot, Bedrock and more, with inline allow/restrict/block. | Enterprises already using Forcepoint / broad DLP |
| Netskope | Netskope One DLP + AI Guardrails inspect prompts/responses and can block source code, IP, PII, etc. across public GenAI. | Large enterprise / SSE-CASB stack |
| Cyberhaven | Uses data lineage/provenance to understand where data originated and follows it into AI prompts, browsers, SaaS, IDEs and agents. | You care about “where did this proprietary data come from?” |
| Check Point / Lakera | AI Guardrails can detect sensitive data in LLM inputs/outputs, mask or block it, and prevent PII from reaching third-party LLM providers. Its Workforce AI Security covers employee use of public AI tools. | AI security + workforce controls |
| Microsoft / Purview | DLP across Microsoft 365, endpoints and supported web/cloud applications, with increasing controls for AI/Copilot and third-party AI. | Microsoft-heavy organizations |
Your problem sounds more specific than conventional DLP:
“An employee takes proprietary information from our systems and pastes it into ChatGPT/Claude/Gemini/etc. We need to know that it happened—and ideally stop it.”
Traditional DLP often misses this because there's no file transfer. The sensitive information can simply be copied from a document and pasted into an LLM text box. Cyberhaven describes this exact gap, while Nightfall, Netskope and Forcepoint now explicitly inspect AI prompts at the point of interaction.
There are actually three different products hiding under “GenAI DLP”:
Employee → public LLM protection
“Did Alice paste source code/customer data into ChatGPT?”
→ Nightfall, Forcepoint, Netskope, Cyberhaven, Check Point/Lakera
Data lineage / forensic detection
“Where did this information originate, and how did it get into the LLM?”
→ Cyberhaven is particularly differentiated here.
Your own LLM application → model protection
“Our application sends customer data to an LLM; prevent sensitive data from reaching the model or leaking in responses.”
→ Lakera/Check Point, Netskope, etc.
If your primary requirement is “tell me when our proprietary data is being leaked into public LLMs”, I'd put these three at the top of an evaluation:
1. Nightfall — strongest purpose-built/AI-native DLP angle. It specifically advertises prompt inspection and data lineage across AI applications.
2. Cyberhaven — strongest if you need to establish exactly what corporate data was copied into the AI interaction and where it came from, rather than merely matching sensitive strings.
3. Netskope — strongest if you want this as part of a broader SSE/CASB/DLP architecture, particularly if you already have Netskope. It says it can inspect every prompt and response and enforce DLP in real time.
Forcepoint is also very compelling if you already have its DLP footprint; its current AI Data Security product now explicitly covers prompts, responses and uploads across multiple major LLMs.
If you tell me your existing stack (e.g. Microsoft, CrowdStrike, Netskope, Zscaler, Palo Alto, Forcepoint, etc.) and whether you're trying to detect past leakage or prevent future leakage, I can build you a vendor-by-vendor comparison of ~10 GenAI DLP products, including deployment method, ChatGPT/Claude/Gemini coverage, endpoint/browser visibility, detection of proprietary data, blocking, and pricing model.
