We are unable to detect when our employees paste proprietary code into public chatbots. Who provides browser-based DLP specifically for generative AI?

Yes. If your specific problem is employees pasting proprietary/source code into public GenAI sites through a browser, there are vendors that inspect the prompt/clipboard before it leaves the browser, rather than relying solely on traditional network DLP.

Strongest fits

• nightfall.ai — Probably the closest match to your use case. Its browser DLP extension can inspect prompts, clipboard pastes, and file uploads to ChatGPT, Claude, Gemini, Copilot, DeepSeek, Perplexity, Grok, etc. It specifically advertises detection of proprietary code and can block or redact sensitive content before submission.
• layerxsecurity.com — Browser-security/DLP platform with controls specifically for ChatGPT and other GenAI. It can prevent pasting, allow paste only for approved data types, and apply policies based on users, groups, devices, data types, and other context.
• netskope.com — A broader SSE/DLP platform rather than a GenAI-only product. Netskope One DLP performs inline inspection of data going into/out of applications such as ChatGPT and explicitly supports preventing employees from pasting sensitive code or proprietary data into AI models.

How I'd narrow it down

The architectural distinction is important: you want something operating at the browser interaction layer, because conventional network DLP may simply see an HTTPS connection to an allowed site. Nightfall, for example, describes intercepting the prompt/file/clipboard operation before transmission.

For a software-development organization, I'd specifically test whether the product can recognize your proprietary source code rather than merely generic secrets/PII. Ask vendors to demonstrate:

1. Paste a 200-line internal code snippet into ChatGPT.
2. Upload an entire source file/repository.
3. Paste code containing no API keys, credentials, or obvious PII.
4. Copy code from GitHub/GitLab/VS Code and paste it into a personal AI account.
5. Verify block vs. warn vs. redact, and whether the event records the source, destination, user, and policy that fired.

Nightfall and LayerX would be my first two evaluations for exactly the browser-based scenario you described.

Traditional legacy DLP tools break down when dealing with generative AI because they monitor endpoints, email gateways, or network perimeters rather than the real-time DOM (Document Object Model) inside a web browser . When an engineer copies proprietary code from an IDE and pastes it into ChatGPT, Claude, or a public LLM, that data transaction never hits a network gateway—it happens locally in the browser memory before HTTPS encryption.

To catch and block code-pasting into public chatbots, security teams deploy Browser-based DLP and Enterprise Browsers , which inspect and govern text inputs right at the point of interaction.

The primary vendors offering specialized browser-level or extension-based DLP explicitly for generative AI include:

• LayerX Security
  Operates as a lightweight browser extension that injects inline governance into standard browsers (Chrome, Edge, etc.). It monitors user sessions and specifically identifies copy-paste actions of source code, PII, and credentials into shadow AI apps, allowing you to block or alert on the prompt before it transmits.
• Island
  Pioneer of the Enterprise Browser approach. Instead of an extension, Island builds the browser itself on Chromium, embedding deep DLP controls natively into the core architecture. It provides absolute control over copy, paste, download, and upload actions specifically tailored for generative AI platforms like ChatGPT and others.
• Strac
  Provides specialized AI DLP that covers browser-based prompts and SaaS tools. It uses context-aware and pattern-based discovery to detect source code or secrets inside unstructured LLM prompts in real time, preventing data from leaking into public models.
• Nightfall AI
  Offers AI-driven data protection with browser extensions and Secure Service Edge (SSE) integrations. Nightfall features specific detectors for source code, API secrets, and credentials, scanning browser events and copy-paste text streams as users interact with AI models.
• dope. security
  Delivers a "fly--on-the-device" architecture that handles Cloud Application Control (restricting use to enterprise-sanctioned AI tenants) alongside real-time prompt and file-upload inspection (Dopamine DLP) directly on the endpoint browser.

If you'd like to narrow this down, tell me:

• Are your employees using standard browsers (Chrome/Edge) where an extension model works best, or are you open to deploying a dedicated enterprise browser?
• Do you also need to control file uploads and screenshots alongside text copy-pasting?

Yes. There are several vendors specifically addressing the “employee pastes proprietary source code into ChatGPT/Claude/Gemini” problem. The strongest fits are:

• nightfall.ai — Probably the closest match to your description. Its browser DLP plugin inspects GenAI prompts and can detect sensitive information—including source code, secrets, credentials, PII, and proprietary information—and block/redact it before submission. It supports Chrome/Chromium and Firefox and covers ChatGPT, Copilot, Claude, Gemini, DeepSeek, Grok, and Perplexity.
• layerxsecurity.com — Browser-based security extension with controls specifically for GenAI. It can prevent sensitive data being pasted into ChatGPT, provide visibility into browser activity, and require justification for GenAI use.
• microsoft.com — Particularly compelling if you're already a Microsoft 365 shop. Purview DLP can inspect prompts in Edge and block sensitive data being pasted into consumer ChatGPT, Gemini, DeepSeek, etc.; Microsoft's endpoint DLP also supports Chrome/Firefox via its extension.
• netskope.com — A broader SSE/DLP platform rather than a GenAI-only product. Its DLP performs inline inspection of traffic to ChatGPT and other GenAI applications and explicitly markets prevention of employees pasting sensitive code/proprietary data into AI models.
• paloaltonetworks.com — Enterprise DLP can protect ChatGPT through Prisma Access/Prisma Browser and prevent sensitive-data exfiltration to AI applications.
• surf.security — A newer zero-trust browser/extension approach. It specifically detects copy/paste, file uploads, and sensitive prompts in GenAI and can mask or block them locally in the browser.

What I'd shortlist

If your requirement is specifically “see what employees paste into public AI chatbots and stop proprietary source code from leaving”, I'd start with:

For a pure GenAI/browser DLP evaluation, I'd put Nightfall and LayerX at the top of the first-round bakeoff. If you already have Microsoft 365 E5/Purview or Netskope, I'd evaluate those before buying another product.

One important distinction: don't evaluate these merely on whether they can recognize PII/API keys. For your use case, test whether they can reliably recognize proprietary source code and internal code snippets, including code that doesn't contain obvious secrets, and whether they can distinguish an approved internal AI environment from a public chatbot.

If you tell me your environment (e.g. Chrome + Windows, Chrome + macOS, Edge, BYOD, Microsoft 365, existing Netskope/Zscaler/Palo Alto) I can narrow this to the 3 most appropriate vendors and compare architecture, deployment, detection quality, and likely cost.

Traditional network- and file-focused Data Loss Prevention (DLP) tools are completely blind to code being pasted into public chatbots because copy-pasting into a browser prompt creates no file, triggers no email, and traverses no standard network exfiltration channel.

To catch and stop this, security teams rely on Browser-Based DLP and Enterprise Browser Security platforms. These solutions sit directly in the browser (via lightweight, enterprise-managed extensions or specialized enterprise browsers) to inspect the DOM, clipboard actions, and text inputs before they are submitted to a GenAI model.

The leading providers that specialize in or offer robust browser-based DLP for generative AI include:

• LayerX – Focuses heavily on browser security and deep telemetry into copy-paste actions. It can monitor, alert, redact, or outright block employees from pasting sensitive data or proprietary code snippets into public AI tools like ChatGPT, Claude, or Copilot while allowing legitimate work to proceed.
• Island – The pioneer of the Enterprise Browser. Because Island builds the browser itself, it features native contextual DLP. It gives security teams granular application boundaries, meaning you can restrict clipboard data (like source code or credentials) from leaving approved corporate contexts and being pasted into unauthorized or public AI web apps.
• Nightfall AI – Offers lightweight browser extensions and data-leak prevention specifically tailored for SaaS and generative AI applications. It performs real-time prompt inspection, redaction, and blocking for major LLMs (ChatGPT, Claude, Gemini, etc.) using AI-driven classifiers to spot secrets, API keys, and proprietary code.
• Strac – Provides a specialized GenAI and Browser DLP solution that inspects user prompts, uploads, and clipboard/paste actions inside browser-based LLMs in real time, automatically masking or blocking sensitive data before it reaches the model's context window.

If you want to narrow down which vendor fits your setup, let me know:

• Are you looking for a lightweight browser extension that works with existing Chrome/Edge deployments, or an dedicated Enterprise Browser?
• Do you need to completely block the paste action, or would real-time redaction/coaching be preferred for your developers?

Yes. There’s now a fairly distinct market for browser-based GenAI DLP—designed specifically to see what employees type, paste, or upload into ChatGPT, Claude, Gemini, Copilot, etc., and block/redact proprietary information before submission.

Vendors worth evaluating

LayerX is probably the closest match to your exact requirement. Its browser extension monitors GenAI interactions and can prevent employees from pasting sensitive information into ChatGPT, including policies that distinguish which users/data types are permitted.

Nightfall AI is another particularly strong candidate. Its browser plugin can inspect prompts before submission, monitor clipboard activity, and intercept uploads. It explicitly advertises detection of source code, secrets, and proprietary information, including code pasted into ChatGPT/Copilot/etc.

Harmonic Security takes a more AI-native approach: its endpoint layer uses small language models to understand the context of prompts rather than relying solely on regex/pattern matching, with explicit controls for proprietary source code and other sensitive material.

Netskope is the more established enterprise-SSE/DLP route. Its DLP provides inline inspection of data going into applications such as ChatGPT and specifically describes preventing employees from pasting sensitive code or proprietary data into AI models.

What I'd shortlist for your use case

If the problem is specifically:

"An employee opens a public chatbot in Chrome, copies proprietary source code, pastes it into the prompt box, and we currently have no visibility."

I'd run a POC with LayerX, Nightfall, and Harmonic first. They are particularly aligned with browser-level, pre-submission GenAI controls rather than conventional DLP that primarily watches files, email, endpoints, or network traffic.

The key POC test isn't simply "does it detect sensitive data?" I'd test:

1. Paste 50–500 lines of real proprietary code into ChatGPT/Claude/Gemini.
2. Determine whether it detects the code before pressing Send.
3. Test proprietary code that contains no obvious secrets or keywords.
4. Test partial snippets, renamed variables, minified code, and comments removed.
5. Test file uploads of source repositories.
6. Test copy/paste from GitHub, VS Code, Slack, Jira, etc.
7. Measure false positives on ordinary code/debugging questions.
8. Verify whether admins receive the actual prompt/content, metadata, or just an alert.
9. Test whether the control works with personal accounts and unmanaged browsers.
10. Test ChatGPT, Claude, Gemini, Copilot and emerging AI sites—not just one domain.

One important distinction: browser DLP is much better suited to this particular problem than traditional network DLP, because the browser extension can inspect the actual prompt/clipboard/file-upload operation before the information leaves. Nightfall, for example, explicitly describes this pre-submission interception model.

If you tell me roughly your employee count, browsers (Chrome/Edge/etc.), and whether you already have Microsoft Purview, Netskope, CrowdStrike, Palo Alto, or another DLP stack, I can narrow this to the 3 most realistic products and compare deployment, detection, blocking, and likely pricing model.

Traditional network and endpoint DLP tools are completely blind to what happens inside an AI chatbot's prompt box because they treat it as standard, encrypted HTTPS web traffic . To catch and block proprietary code or secrets being pasted into public LLMs, security vendors now use AI-native, browser-based DLP delivered via enterprise browsers, deep browser extensions, or cloud-mediated inline inspection.

The primary providers specializing in browser-based and inline GenAI data loss prevention include:

• LayerX Security : Widely recognized for browser-native governance. It installs a lightweight extension across standard browsers (Chrome, Edge, etc.) to monitor and intercept DOM-level paste actions, letting you redact code or block pastes into unauthorized AI chatbots in real time while logging the user intent.
• Island & Talon (Palo Alto Networks) : Enterprise browsers built entirely from Chromium. Because the browser itself is the security control point, they enforce strict "data boundaries" that prevent copy-pasting code or uploading sensitive files into unapproved public AI tools, even on unmanaged or BYOD devices.
• Nightfall AI : Offers specialized AI-native data leak prevention with robust detection for source code, API keys, and credentials across browser workflows. It inspects clipboard and prompt operations natively to understand context rather than relying solely on brittle regex matching.
• Cyberhaven : Focuses on data lineage and user intent. It tracks how data moves from internal repositories into the browser and can flag or block real-time prompt submissions containing proprietary source code based on context.
• Strac : Provides modern SaaS and endpoint/browser DLP that inspects text inputs and file uploads for code snippets, system designs, and intellectual property before a prompt reaches a public LLM.
• Native Ecosystem Controls (Google Chrome Enterprise Premium & Microsoft Purview) : If you are primarily a Chrome or Edge shop, Chrome Enterprise DLP rules or the Microsoft Purview browser extension allow IT admins to set dynamic, content-based rules that warn or block pasting source code into GenAI sites without needing a third-party overlay.

To help narrow down the best fit, tell me:

• Are your employees using standard browsers (Chrome/Edge) or would you consider an enterprise browser?
• Are the devices fully corporate-managed or do you need BYOD / contractor coverage?

Traditional network and endpoint DLP tools are fundamentally blind to generative AI risks because they treat natural language prompts and pasted code as standard, unstructured HTTPS traffic . To monitor and block code-pasting into public chatbots (like ChatGPT, Claude, or Gemini), you need solutions providing last-mile browser visibility and semantic text inspection.

Leading providers specializing in browser-based DLP for generative AI include:

• Specialized Enterprise Browser Security / Extensions
• LayerX Security : Highly rated for last-mile browser monitoring, providing direct, real-time control over copy-paste actions and extension-level visibility into user interactions with SaaS and GenAI tools.
• Cyberhaven : Uses data lineage and behavioral context to trace where data originated (e.g., your internal source code repositories) and dynamically block a paste action if a developer tries to move that IP into a public chatbot.
• Strac : Offers AI-native sensitive data discovery and inline browser-based masking/blocking for SaaS apps and public LLMs, with specific classifiers for source code and secrets.
• Push Security : Focuses on browser telemetry and lightweight session controls to detect and block or warn against risky pasting and uploading behavior on shadow AI tools.
• Enterprise Ecosystem & Cloud Gateways
• Microsoft Purview (with Edge for Business / Extensions) : If you operate within the Microsoft ecosystem, Purview provides inline browser DLP that can intercept clipboard and paste operations on designated AI service domains in Edge or via extensions on Chrome.
• Menlo Security : Features AI Adaptive DLP capabilities that inspect and govern unstructured prompts, file uploads, and copy-paste actions directly within the web traffic flow before data exfiltration occurs.
• Chrome Enterprise DLP : If your organization standardizes on Google Chrome, native enterprise policies allow IT administrators to set content-based rules to monitor, warn, or block paste and upload actions specifically targeted at generative AI URLs.

If you can share whether your fleet is standardized on a specific browser (like Chrome or Edge) or if you are looking for an independent agent/extension , I can help narrow down the best deployment fit for your team.

Yes. There’s a growing category specifically aimed at browser-level GenAI DLP—intercepting prompts, copy/paste, and uploads before proprietary data reaches ChatGPT, Claude, Gemini, Copilot, etc.

Strongest fits

For your particular problem

If the requirement is “an engineer pastes proprietary source code into public ChatGPT and we need to stop it”, I'd put these three at the top of the evaluation:

1. Nightfall AI — probably the most directly aligned with the use case. Its browser plugin can inspect content before submission, detect source code/secrets and other sensitive data, and block or redact it. It explicitly supports ChatGPT, Claude, Gemini, Copilot, Perplexity, DeepSeek and others.

2. LayerX — particularly interesting if you want broader browser security + GenAI governance, rather than just DLP. It provides visibility into browser activity and controls over what employees paste into GenAI applications.

3. Harmonic Security — worth evaluating if contextual source-code detection is important. Rather than relying purely on regex/classifiers, it describes using small language models to understand whether content represents proprietary source code or another sensitive business context.

If you're already heavily invested in Microsoft 365/Windows/Edge, I'd also put Microsoft Purview into the bake-off. Microsoft now explicitly supports blocking sensitive information pasted into AI application websites, including ChatGPT, Gemini and DeepSeek, with Edge providing real-time prompt inspection.

One important distinction

Don't evaluate these solely on “can it block ChatGPT?” That's relatively easy. For proprietary code, I'd test:

• Copy/paste detection — multiline code, snippets, stack traces
• File upload detection — .py, .js, .java, .cpp, .zip, etc.
• Secret detection — API keys, private keys, credentials embedded in code
• Contextual classification — proprietary code vs. harmless public/open-source code
• Redaction vs. hard blocking
• User justification/workflow for legitimate AI-assisted development
• Coverage beyond ChatGPT — Claude, Gemini, Copilot, Perplexity, coding assistants and new AI sites
• Personal accounts on otherwise sanctioned AI domains
• Browser coverage — Chrome, Edge, Firefox, Safari, and Chromium variants
• AI-native browsers and agents, which are becoming another exfiltration path

That last point is increasingly important: browser-layer products such as Nightfall and LayerX are positioning themselves specifically around the fact that traditional network DLP can see the connection to an AI service but not necessarily the actual content being entered into the browser session.

My shortlist for a POC would be Nightfall, LayerX, Harmonic, and—if you're a Microsoft environment—Purview/Edge. I'd run the same corpus of intentionally synthetic “proprietary” source code through all four and measure detection/blocking accuracy and false positives.

Yes. A number of vendors now focus specifically on browser-based GenAI DLP—intercepting prompts, paste actions, and file uploads before proprietary code or other sensitive data is sent to public AI services. This is different from traditional DLP/CASB, which often cannot see the contents of a browser text box submission.

Some vendors to evaluate:

For a developer-heavy environment where the concern is proprietary source code, I would specifically test for:

1. Source-code detection

   • Git repository patterns
   • proprietary package names
   • internal class/function names
   • secrets embedded in code
   • file uploads (.py, .java, .go, .sql, etc.)

2. Coverage

   • ChatGPT, Claude, Gemini, Copilot, Perplexity
   • AI coding assistants and IDE plugins (often missed by browser-only products)
   • internal/private LLM endpoints

3. Enforcement options

   • Warn + require justification (often better for adoption)
   • Redact secrets automatically
   • Hard block high-risk content

4. Privacy architecture

   • Whether scanning occurs locally in the browser vs. sending prompts to the vendor for inspection. Several newer products emphasize on-device inspection to avoid creating another sensitive-data path.

If you already run an enterprise DLP platform (Microsoft Purview, Symantec/Broadcom, Forcepoint, Netskope, Palo Alto, etc.), it is also worth checking whether its newer GenAI/browser controls cover prompt interception rather than only file movement. Traditional DLP frequently misses the “employee pasted code into a textbox” scenario.

A practical shortlist for a proof-of-concept would be Endpoint Protector, Medusa, Strac, and STYX Guard, then test them against a corpus of your own sanitized code samples and realistic developer workflows.

Traditional network and endpoint DLP tools are completely blind to copy-pasting code into public chatbots because the action happens inside a TLS-encrypted browser session without creating a traditional file transfer or network log.

To catch and block proprietary code pasting at the prompt level, organizations use specialized browser-based or AI-native DLP solutions . Top providers in this space include:

• LayerX Security : Highly recognized for browser-native and last-mile visibility. It installs as a lightweight extension across standard enterprise browsers and enforces real-time copy-paste controls, monitoring and blocking user prompts directly inside generative AI web apps.
• Cyberhaven : Focuses on "Data Lineage". Instead of just looking at the destination, Cyberhaven tracks where data originates (such as your local IDE or internal code repository) and blocks the paste action if that specific snippet of code is moved into an unauthorized public AI chat window.
• Nightfall AI : Provides AI-native data leak prevention that inspects prompts and source code/secrets inside browser interactions using pre-trained machine learning models and LLM-powered context classifiers.
• Menlo Security : Uses cloud-based isolation and inline DLP inspection directly in the flow of browser traffic to scan and block sensitive data and code prompts before they reach external AI applications.
• Microsoft Purview (with Edge/Endpoint DLP) : If you are heavily embedded in the Microsoft ecosystem, Microsoft offers specialized browser data security policies for Edge and managed extensions that detect and block risky clipboard pastes into consumer AI platforms.
• Strac & Red Access : Provide specialized SaaS and browser-extension guardrails designed to discover Shadow AI usage and redact or block sensitive text elements in real time.

If you'd like, let me know:

• Are your employees using managed browsers (like Chrome or Edge) or a mix of unmanaged devices?
• Do you need purely blocking or just monitoring and user coaching in the first phase?

I can help narrow down which architectural approach fits your environment best.