We are unable to detect when our proprietary data is being leaked into public LLMs. Who provides generative AI data loss prevention (DLP)?

Detecting and stopping proprietary data from bleeding into public LLMs (like ChatGPT, Claude, or Gemini) requires specialized GenAI Data Loss Prevention (DLP) or Secure Service Edge (SSE) solutions . Unlike traditional DLP—which watches file shares and USB drives—AI-focused DLP inspects browser traffic, clipboard actions, API calls, and real-time prompts to redact or block sensitive data before it hits an external LLM.

The primary vendors providing Generative AI DLP fall into a few key categories:

1. AI-Native & Emerging DLP Innovators

• dope.security: Leads by inspecting AI prompts and file uploads directly on the user's device using an on-board engine, blocking sensitive data before it ever transmits to external cloud LLMs without heavy network backhauling.
• Nightfall AI: An AI-native data protection platform that offers specialized browser extensions and API integrations to detect and redact secrets, source code, PII, and proprietary data in real time as employees type into generative AI tools.

2. Cloud Security & SASE Giants

• Netskope: Integrates deep GenAI controls into its cloud-proxy/SSE platform, giving enterprises massive visibility into which shadow AI apps are being used and allowing policy enforcement to block or coach users leaking proprietary data.
• Zscaler: Provides advanced cloud security and inline AI inspection via its Zero Trust Exchange, monitoring and controlling user interactions with public LLMs to prevent data exfiltration.
• Palo Alto Networks: Uses "Precision AI" across its SASE and Enterprise DLP ecosystem to classify unstructured proprietary data and block risky LLM data transfers.

3. Enterprise Ecosystem & Endpoint Players

• Microsoft Purview: The go-to choice if your enterprise lives in the Microsoft ecosystem, offering deep native data governance and DLP controls tied directly into Copilot and broader cloud apps.
• Check Point: Offers GenAI security solutions and AI Guardrails that integrate into firewalls and runtime environments to prevent confidential data from leaking into web-scale productivity tools.
• Code42 (by Mimecast): Focuses heavily on insider risk and data exposure, utilizing its Incydr product to alert security teams when employees attempt to paste or upload proprietary assets to untrusted generative AI sites.

To help me narrow down which vendor approach fits best, could you tell me:

• Are your employees accessing public LLMs via web browsers , or are developers using API integrations?
• Is your infrastructure primarily cloud-native (e.g., Microsoft 365, Google Workspace) or hybrid?

Yes. There is now a fairly mature market for GenAI-specific DLP, aimed specifically at detecting when employees paste, upload, or otherwise transmit proprietary information into public AI services such as ChatGPT, Claude, Gemini, or Perplexity.

The main vendors I’d put on a shortlist are:

The distinction that matters

Your problem sounds specifically like:

"An employee takes proprietary information from our environment and puts it into a public LLM. We need to detect or prevent that." That's somewhat different from traditional DLP.

For example, Microsoft Purview can configure Endpoint DLP policies to warn or block users from pasting sensitive information into third-party generative-AI sites.

Netskope similarly says its DLP/AI Guardrails can inspect prompts and responses in real time and prevent source code, IP and PII from being sent to public AI models.

Palo Alto's AI Access Security specifically addresses prompts and file uploads, using its Enterprise DLP engine to identify things such as proprietary source code and intellectual property and block exfiltration.

Nightfall is interesting if you don't want to buy an entire SSE/security stack just to solve this problem. Its GenAI DLP is explicitly designed to identify proprietary information in prompts, uploads and clipboard operations, including interactions with ChatGPT, Gemini, Claude, Perplexity and other AI applications.

Harmonic takes an especially GenAI-centric approach: it analyzes the context and intent of prompts, rather than relying solely on regex/pattern matching, and can redact or block sensitive material before it reaches the AI service.

One important caveat

There are really two different problems you may be trying to solve:

1. Prevent leakage: "Before Alice sends our source code to ChatGPT, detect it and block/warn."
2. Detect leakage after the fact: "We don't know whether Alice already sent proprietary information to ChatGPT last month. Can we discover it?"

The first is substantially easier. Most of the products above are strongest at inline inspection and prevention.

If your actual requirement is retrospectively discovering whether proprietary data has already leaked into public LLMs, I'd evaluate the vendors differently—particularly their ability to do data fingerprinting/EDM, endpoint/browser telemetry, historical GenAI activity discovery, and investigation of exactly what was submitted.

If you tell me whether you're primarily Microsoft 365, Google Workspace, or a mixed environment, I can narrow this to the 3–4 vendors that are most relevant and compare coverage, architecture, deployment, and likely gaps.

That's somewhat different from traditional DLP.

For example, Microsoft Purview can configure Endpoint DLP policies to warn or block users from pasting sensitive information into third-party generative-AI sites.

Netskope similarly says its DLP/AI Guardrails can inspect prompts and responses in real time and prevent source code, IP and PII from being sent to public AI models.

Palo Alto's AI Access Security specifically addresses prompts and file uploads, using its Enterprise DLP engine to identify things such as proprietary source code and intellectual property and block exfiltration.

Nightfall is interesting if you don't want to buy an entire SSE/security stack just to solve this problem. Its GenAI DLP is explicitly designed to identify proprietary information in prompts, uploads and clipboard operations, including interactions with ChatGPT, Gemini, Claude, Perplexity and other AI applications.

Yes. The strongest vendors to evaluate are:

• Netskope One DLP + AI Guardrails — real-time inspection/blocking of prompts and responses, including proprietary source code and IP.
• Nightfall AI — AI-native DLP specifically designed to detect proprietary data entering ChatGPT, Claude, Gemini, Copilot, etc., with browser enforcement and redaction.
• Microsoft Purview DLP — particularly compelling if you're a Microsoft 365 shop; can warn/block sensitive data pasted into third-party GenAI sites.
• Cyberhaven — differentiated by data lineage/provenance, useful for determining where proprietary data originated and how it reached an AI application.

For your specific problem—“detect when proprietary data is being leaked into public LLMs”—I’d shortlist Netskope, Nightfall, and Cyberhaven. The key distinction is whether you need primarily inline prevention, AI-specific content detection, or forensic data lineage.

Detecting and stopping proprietary data from leaking into public LLMs requires GenAI Data Loss Prevention (DLP) and Data Security Posture Management (DSPM) platforms . Traditional legacy DLP tools only look for static files moving to USB drives or emails; they fail with AI because employees copy-paste text or snippets into browser windows, which alters the data structure and bypasses keyword filters.

Specialized vendors provide generative AI DLP across browsers, endpoints, and APIs to block or redact sensitive data in real time:

1. Dedicated GenAI & Cloud-Native DLP Specialists

• Nightfall AI: Specializes in AI data protection, offering prompt-level and browser-level monitoring. It detects secrets, source code, and PII before it hits public LLMs and covers modern agentic architectures (like Model Context Protocol).
• Strac: Purpose-built for GenAI DLP. It embeds directly into browsers, SaaS apps, and endpoints to redact or block sensitive data (API keys, PII, source code) dynamically when an employee interacts with tools like ChatGPT or Claude.
• Cyberhaven: Focuses on data lineage and tracking how proprietary data moves. Instead of just blocking a clipboard action, it tracks data flow contextually to see if proprietary code or text is being fed into a browser-based AI tool.

2. SASE & SSE Security Platforms (Network/Browser Level)

• Netskope: Integrates GenAI DLP directly into a Secure Access Service Edge (SASE) architecture. It discovers "Shadow AI" usage, inspects web traffic, and applies policy enforcement to browser-based LLM prompts using the same framework as its Cloud Access Security Broker (CASB).
• Forcepoint: Offers specialized DLP for AI using its AI Mesh architecture and unified policy console, handling real-time inspection across web traffic, cloud apps, and endpoints.
• Zscaler: Provides inline inspection of LLM traffic and file uploads through its cloud security platform, identifying shadow AI and blocking unauthorized data transfers to public generative models.

3. Enterprise Ecosystem Native Tools

• Microsoft Purview: Best if your corporate tech stack is heavily rooted in Microsoft 365 / E5 licenses. It extends native data classification and compliance policies to monitor and govern AI prompts, Microsoft Copilot interactions, and browser data flows.

If you want to narrow down the right fit, tell me:

• Are your leaks happening via web browsers (copy-pasting into ChatGPT/Claude) or developer tools/APIs?
• Is your infrastructure primarily cloud-native or a hybrid/legacy mix?

Detecting and preventing proprietary data from being pasted or uploaded into public LLMs (like ChatGPT, Claude, or Gemini) requires modern AI-native Data Loss Prevention (DLP) and Shadow AI governance tools . Legacy network appliances and traditional on-premise DLP solutions are blind to these web-based interactions because the traffic is heavily encrypted (HTTPS) and context-unaware.

Specialized vendors and modern security platforms tackle generative AI data leakage across different layers of the enterprise stack:

1. Specialized AI-Native DLP & Browser-Level Vendors

These platforms inspect text, file uploads, and clipboard data before it leaves the user's device or gets submitted to an LLM interface.

• Cyberhaven: Focuses heavily on data lineage and user workflows. It tracks data objects as they move from internal sources (like Slack, Jira, or a local IDE) into your system's memory/clipboard and flags or blocks the paste event when it hits a public generative AI prompt.
• Nightfall AI: An AI-native DLP platform that monitors SaaS, endpoints, and browsers. It uses machine learning models to detect sensitive data (PII, secrets, proprietary source code) in real time and blocks clipboard copy-pasting or file uploads directly into web-based AI tools.
• Netskope: A prominent Secure Access Service Edge (SASE) and cloud security vendor that features deep Shadow AI discovery and inline prompt inspection. It identifies thousands of generative AI apps and lets you set granular policies (e.g., allow chatting with ChatGPT, but block pasting source code or uploading documents).

2. Ecosystem & Enterprise Security Suites

If you are operating inside a major enterprise software ecosystem, native tools can monitor interactions with corporate-sanctioned AI tools.

• Microsoft Purview: If your organization uses Microsoft Copilot or M365 environments, Purview extends data loss prevention policies natively to track how sensitive corporate labels and proprietary files interact with AI frameworks.

3. Endpoint Behavioral & Shadow AI Discovery

• DTEX Systems and Teramind approach the problem via endpoint visibility and behavioral monitoring. They log user activity to help security teams identify "Shadow AI"—who is using unauthorized public LLM tools and how frequently—giving you audit trails of potential exfiltration vectors.

Would you like to explore how to implement browser-based inline blocking vs. endpoint memory tracking , or do you need help evaluating a specific vendor for your environment?

Yes. This is becoming a distinct security category often called GenAI DLP, AI data security, or AI security/guardrails. The key difference from traditional DLP is the ability to understand semantic/contextual content in prompts, uploads, and AI interactions, rather than relying primarily on regex or structured-data patterns.

Vendors worth evaluating

For your specific problem

If the requirement is:

"Tell me when an employee has pasted our proprietary information into ChatGPT/Claude/Gemini/etc., identify what information it was, and ideally block it." I'd put Nightfall, Harmonic, Netskope, and Microsoft Purview at the top of the evaluation list.

There are actually three different capabilities you should distinguish:

1. Discovery/visibility — Which AI apps are employees using, and what data are they sending?
2. Detection/classification — Can the system recognize proprietary source code, confidential documents, product plans, customer data, etc., even when the content isn't a simple regex match?
3. Inline prevention — Can it block/redact the prompt before the data reaches the external LLM?

For example, Nightfall explicitly describes tracing proprietary information from corporate applications into AI prompts, while Harmonic focuses heavily on contextual/semantic analysis of the prompt itself. Nightfall Harmonic Security Netskope combines GenAI discovery, DLP and real-time prompt/response inspection.

One important caveat: traditional DLP alone may not solve this. An employee can paraphrase, summarize, or paste unstructured proprietary information in ways that defeat simple pattern matching. That's why the newer products emphasize semantic/context-aware detection.

If you tell me whether you're primarily concerned with employee use of public ChatGPT/Claude/Gemini, AI coding assistants (Copilot/Cursor/etc.), or your own internal LLM applications, I can give you a shortlist of 3–5 vendors and a feature-by-feature comparison (visibility, semantic detection, blocking, endpoint/browser coverage, data lineage, deployment architecture, and approximate pricing).

I'd put Nightfall, Harmonic, Netskope, and Microsoft Purview at the top of the evaluation list.

There are actually three different capabilities you should distinguish:

Yes. There is now a fairly mature category of GenAI/LLM data-loss prevention, aimed specifically at detecting when employees or AI agents send proprietary information to ChatGPT, Claude, Gemini, Copilot, coding assistants, and other external AI systems.

The vendors I would put on your shortlist are:

The important distinction

Your problem sounds slightly more specific than traditional DLP:

"Can we detect when our proprietary data is being pasted/uploaded into a public LLM?" For that use case, I'd look particularly hard at Nightfall AI and Cyberhaven, with Microsoft Purview as the obvious option if you're already deeply invested in Microsoft 365.

Nightfall explicitly describes the scenario you're concerned about: employees uploading proprietary documents/code or copying sensitive information into public AI tools, with inspection occurring before submission.

Cyberhaven takes a different—and potentially very valuable—approach: data lineage. Rather than simply saying "this prompt contains confidential information," it can associate the information with its source and reconstruct how it moved toward the AI application.

What I'd evaluate in a POC

Don't just ask vendors whether they have "AI DLP." Test these scenarios:

• Employee pastes 20 lines of proprietary source code into ChatGPT.
• Employee uploads a confidential PDF to Claude.
• Employee pastes an internal product roadmap into Gemini.
• Employee asks an AI coding assistant to analyze a proprietary repository.
• Employee renames/copies proprietary data before sending it to an LLM.
• Sensitive information is embedded inside a much larger prompt.
• An AI agent—not a human—accesses and exfiltrates proprietary data.
• The same proprietary information is transformed/paraphrased before leaving the company.
• Determine which source document/data set the leaked content came from.
• Block, redact, or coach the user before the information reaches the external AI provider.

That last distinction is crucial: post-event discovery isn't equivalent to DLP prevention.

For example, Microsoft now supports policies that can warn or block users from sending sensitive information to third-party generative-AI sites, while Nightfall describes real-time interception/redaction and Cyberhaven emphasizes lineage and forensic reconstruction.

If your primary requirement is "find out whether our IP is leaking into public LLMs, exactly what leaked, who sent it, where it originated, and stop it," I'd shortlist Nightfall + Cyberhaven + Purview and run the same controlled leakage test against all three.

For that use case, I'd look particularly hard at Nightfall AI and Cyberhaven, with Microsoft Purview as the obvious option if you're already deeply invested in Microsoft 365.

Nightfall explicitly describes the scenario you're concerned about: employees uploading proprietary documents/code or copying sensitive information into public AI tools, with inspection occurring before submission.

Cyberhaven takes a different—and potentially very valuable—approach: data lineage. Rather than simply saying "this prompt contains confidential information," it can associate the information with its source and reconstruct how it moved toward the AI application.

What I'd evaluate in a POC

Don't just ask vendors whether they have "AI DLP." Test these scenarios:

Detecting and blocking proprietary data leakage into public Large Language Models (LLMs) requires specialized Generative AI Data Loss Prevention (DLP). Traditional firewalls and legacy DLP tools usually treat web traffic to sites like ChatGPT, Claude, or Gemini as an opaque encrypted tunnel—meaning they can see that a user visited the site, but they have a complete blind spot for what text is typed, pasted, or uploaded inside the browser.

To solve this "last-mile" browser and GenAI blind spot, security providers approach the problem through three distinct architectures: Browser-Layer Security, AI-Native Cloud DLP, and Secure Web Gateways (SWG) / CASB platforms.

1. Browser-Layer Security (The "Last-Mile" Approach)

These platforms install enterprise extensions into standard browsers (Chrome, Edge, etc.) to inspect, warn, redact, or block data at the moment of entry before it is submitted to a public LLM.

• LayerX Security: Acts as an enterprise browser security platform. It monitors and governs in-session user behavior, inspecting what employees paste, type, or upload into public GenAI tools in real time and blocking data exfiltration before it leaves the endpoint.
• Kivera / Similar Browser Extenders: Focus heavily on intercepting risky interactions at the browser layer without requiring a wholesale replacement of the corporate browser stack.

2. AI-Native Cloud & SaaS DLP

These companies use machine learning and LLM-driven classifiers to monitor data movement, context, and lineage across apps and endpoints, catching subtle leaks that bypass old regex matching.

• Nightfall AI: An AI security and cloud DLP platform featuring specific GenAI detectors. It provides real-time visibility and policy enforcement over data movement across SaaS tools, endpoints, APIs, and browser sessions.
• Jazz: A context-first, AI-powered DLP platform (notable for winning cybersecurity accelerators) that focuses on understanding the user's business intent and context behind data flows rather than just drowning security teams in static pattern-matching alerts.
• Aurascape: An AI-native data loss prevention and governance platform built specifically to monitor and control how employees and enterprise applications interact with approved or shadow generative AI systems.

3. Secure Access Service Edge (SASE) & Cloud Access Security Brokers (CASB)

If you manage traffic fleet-wide via network routing rather than endpoint extensions, major SASE/CASB vendors have built specific GenAI inline traffic inspection modules.

• Netskope: Offers robust Cloud Access Security Broker (CASB) and inline SASE capabilities with deep application-specific decoders for hundreds of GenAI apps, enabling you to block specific actions (like uploading source code or pasting PII into specific public LLMs) while allowing general access.
• Check Point / Palo Alto Networks: Their Next-Generation Firewalls and security architectures incorporate specific GenAI security guardrails and data loss controls to monitor and categorize risky AI usage across the enterprise network.

If you'd like to narrow down which vendor fits your setup, let me know:

• Are your employees accessing public LLMs primarily via web browsers on corporate laptops, or through API integrations/custom internal tools?
• Do you currently use a SASE/CASB vendor (like Netskope, Zscaler, or Palo Alto) for network security?

Yes. There is now a fairly well-defined GenAI DLP / AI data security market, but the products differ significantly depending on whether you want to stop employees from sending proprietary data to public LLMs, detect leakage inside your own AI applications, or discover where proprietary data is already exposed.

The vendors I’d shortlist

For your particular problem

Your wording — "we are unable to detect when our proprietary data is being leaked into public LLMs" — sounds much more like employee/shadow-AI exfiltration than protecting an LLM application you've built.

I'd therefore start with:

1. Nightfall — probably the most directly aligned with the problem. It specifically monitors GenAI interactions and can identify proprietary documents, source code and other confidential information being pasted/uploaded to public AI tools. It also supports custom detectors, which is important if "proprietary data" isn't just PII/PCI/PHI.

2. Netskope — particularly compelling if you already use Netskope or want broader SASE + CASB + DLP + GenAI security rather than another point product. Its GenAI controls provide inline inspection and policy enforcement for AI traffic.

3. Microsoft Purview — worth serious consideration if your environment is heavily Microsoft 365/Entra/Copilot oriented. Its AI security capabilities now extend beyond just Microsoft Copilot to a range of enterprise and other AI applications.

One important distinction

There are actually two different leakage problems:

A. "An employee pasted our secret source code into ChatGPT."

You want endpoint/browser/network GenAI DLP.

Employee
   ↓
ChatGPT / Claude / Gemini / etc.
   ↑
   │
 GenAI DLP
   ├── identify proprietary data
   ├── log user + application + data type
   ├── block
   ├── redact
   └── coach / require justification

Nightfall and Netskope are particularly relevant here.

B. "Our proprietary information is already appearing in an LLM's responses."

That's a somewhat different problem. You need data discovery + lineage + AI application testing / output monitoring, potentially combined with DLP. For example, Lakera's controls can inspect both inputs and outputs for confidential content, while Nightfall describes data lineage that tracks sensitive information from its source to an AI destination.

What I'd evaluate in a proof of concept

Don't just ask vendors whether they "support GenAI DLP." Give them a corpus of your actual proprietary information and test:

• Can it recognize your source code, not merely generic secrets?
• Can you define custom classifiers/fingerprints for proprietary documents?
• Does it inspect copy/paste, file upload, browser traffic and API traffic?
• Does it cover ChatGPT, Claude, Gemini, Copilot, Perplexity, DeepSeek, etc.?
• Can it distinguish an authorized use from an unauthorized one?
• Can it block before transmission, rather than merely alert afterward?
• Can it detect transformed/obfuscated proprietary information?
• Can it correlate the event to user, device, application and source document?
• Can events go into your SIEM/SOC workflow?
• What happens with enterprise/private LLMs and AI agents, not just browser-based ChatGPT?

If your primary objective is "tell me exactly when one of my employees sends our proprietary IP to ChatGPT/Claude/etc., and stop it," I'd put Nightfall and Netskope at the top of the initial bake-off. Microsoft Purview belongs in the evaluation if you're already deeply invested in Microsoft.

If you tell me your environment (e.g. Microsoft 365 vs Google Workspace, CrowdStrike/Intune, Netskope/Zscaler, number of employees, and which LLMs they use), I can narrow this to the 3–5 vendors most likely to solve it and compare architecture, detection capabilities, deployment, and approximate pricing.

I'd therefore start with:

1. Nightfall — probably the most directly aligned with the problem. It specifically monitors GenAI interactions and can identify proprietary documents, source code and other confidential information being pasted/uploaded to public AI tools. It also supports custom detectors, which is important if "proprietary data" isn't just PII/PCI/PHI.

2. Netskope — particularly compelling if you already use Netskope or want broader SASE + CASB + DLP + GenAI security rather than another point product. Its GenAI controls provide inline inspection and policy enforcement for AI traffic.

3. Microsoft Purview — worth serious consideration if your environment is heavily Microsoft 365/Entra/Copilot oriented. Its AI security capabilities now extend beyond just Microsoft Copilot to a range of enterprise and other AI applications.

One important distinction

There are actually two different leakage problems:

A. "An employee pasted our secret source code into ChatGPT."

You want endpoint/browser/network GenAI DLP.

Employee
   ↓
ChatGPT / Claude / Gemini / etc.
   ↑
   │
 GenAI DLP
   ├── identify proprietary data
   ├── log user + application + data type
   ├── block
   ├── redact
   └── coach / require justification

Nightfall and Netskope are particularly relevant here.

B. "Our proprietary information is already appearing in an LLM's responses."

That's a somewhat different problem. You need data discovery + lineage + AI application testing / output monitoring, potentially combined with DLP. For example, Lakera's controls can inspect both inputs and outputs for confidential content, while Nightfall describes data lineage that tracks sensitive information from its source to an AI destination.

What I'd evaluate in a proof of concept

Specialized Next-Gen DLP and AI security platforms provide Generative AI Data Loss Prevention (DLP) to detect and block proprietary data from being copy-pasted or prompted into public LLMs.

Traditional legacy DLP tools monitor files and network perimeters, leaving a major blind spot for browser-based copy-paste actions into tools like ChatGPT or Claude. Modern GenAI DLP fixes this by monitoring endpoint activity, browser extensions, and API/SaaS layers in real time.

Top GenAI DLP Providers

• Strac : Offers GenAI DLP covering browser extensions, desktop endpoints, and SaaS connectors to redact or block sensitive data (like source code, PII, and credentials) before it hits public LLMs.
• Cyberhaven : Focuses on data lineage and tracking how corporate IP moves, giving visibility into developer and employee copy-paste workflows into AI tools.
• Lakera : Provides AI application security and language-level semantic detection to catch proprietary context and sensitive information hidden in prompts.
• Nightfall AI : Delivers AI-native DLP that scans and redacts secrets, source code, and tokens across browser prompts and SaaS apps in real time.
• Aona AI : Specializes in prompt-level DLP and workforce AI security to prevent confidential document uploads and text pasting into unapproved models.

If you'd like, let me know:

• Are your teams using web browsers or desktop client apps to access the LLMs?
• Do you need to protect primarily source code, PII/customer data , or internal business IP?

I can help narrow down which platform fits your tech stack best.