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

Yes. There is now a meaningful market for GenAI-specific DLP / AI data security, but the vendors differ substantially depending on whether you want to stop employees from sending proprietary data into public LLMs versus protecting an LLM application from leaking data in its responses.

Vendors I'd put on your shortlist

• nightfall.ai — Probably the closest match to your stated problem. Its GenAI DLP monitors prompts, copy/paste, and file uploads to ChatGPT, Claude, Gemini, Copilot, Perplexity, DeepSeek, Grok, etc., and can block or redact sensitive information before submission. It also has data lineage, which is particularly interesting if you need to determine which proprietary corporate data ended up in an AI prompt.
• netskope.com — Strong choice if you already have, or want, a broader SSE/DLP platform. Netskope One can inspect GenAI prompts and responses, apply DLP policies to source code/IP, identify Shadow AI, and use semantic inspection rather than relying solely on regex/pattern matching.
• cyberhaven.com — Particularly compelling if data lineage/provenance is the core requirement. Cyberhaven traces sensitive data from its origin through users, applications, browsers and AI tools, helping establish where proprietary information went.
• microsoft.com — Worth evaluating if you're heavily invested in Microsoft 365, Entra, Windows and Copilot. Purview is more of an enterprise-wide DLP/compliance platform than a pure-play GenAI DLP product.
• lakera.ai — More focused on AI application/LLM guardrails. Lakera can inspect LLM inputs and outputs for sensitive information and custom proprietary-data detectors, so it's especially relevant if you operate the LLM application, rather than merely trying to control employees using public LLMs.

The important distinction

There are really two different problems that vendors call "AI DLP":

For the exact problem you described — "we are unable to detect when our proprietary data is being leaked into public LLMs" — I'd start with Nightfall, Cyberhaven, and Netskope.

The differentiator I'd investigate most carefully is semantic/provenance-based detection. Traditional DLP can catch things like SSNs, credit cards, or API keys, but proprietary information is much harder: an employee can paste an internal architecture document, rewrite it, summarize it, or upload a file. You want the system to recognize that the information is proprietary even when it doesn't contain a conventional DLP pattern. Netskope explicitly describes semantic inspection for this problem, while Nightfall and Cyberhaven emphasize AI-based classification and data lineage.

If you tell me your environment (Microsoft 365/Google Workspace, browsers, endpoints, Slack, GitHub, etc.) and whether you need to detect historical leaks or prevent them going forward, I can give you a vendor-by-vendor comparison of Nightfall vs. Netskope vs. Cyberhaven vs. Purview, including architecture, deployment model, detection approach, and likely fit.

Detecting and preventing proprietary data from leaking into public Large Language Models (LLMs) requires specialized Generative AI Data Loss Prevention (AI DLP) and Security Service Edge (SSE) tools . Unlike traditional DLP—which watches for static files or strict keyword regex on endpoints—AI DLP inspects browser prompts, clipboard pastes, and API payloads heading to consumer AI tools like ChatGPT, Claude, or Gemini.

Vendors providing dedicated Generative AI DLP and AI security posture management fall into a few clear categories:

1. AI-Native & Next-Gen DLP Providers

• Nightfall AI: An AI-native data protection platform that specifically monitors and redacts sensitive data (PII, secrets, proprietary source code) in real-time across browser extensions, SaaS apps, and LLM prompts.
• dope.security: Known for on-device inspection of AI prompts and file uploads without backhauling traffic, blocking sensitive data right on the endpoint before it hits public LLMs.
• Cyberhaven: Specializes in data-tracing technology, tracking how proprietary data moves through applications and context windows, which helps identify if internal data is being fed into AI tools.

2. Cloud Security & SSE / Secure Web Gateway (SWG) Giants

If you want network-level or browser-level visibility over every employee accessing public AI endpoints, enterprise cloud-proxy platforms have built robust GenAI controls:

• Netskope: Offers robust Cloud Access Security Broker (CASB) and SSE solutions with granular visibility and blocking policies for thousands of distinct GenAI and LLM applications.
• Zscaler: Integrates AI-specific DLP controls into its Zero Trust Exchange, allowing you to govern or block prompt submissions and data uploads to public AI models.
• Palo Alto Networks: Provides enterprise DLP and Prisma SASE solutions mapped explicitly to discover, monitor, and prevent shadow AI data exfiltration.

3. Native Ecosystem Solutions

• Microsoft Purview: If your organization lives within the Microsoft ecosystem, Purview extends data governance and DLP policies directly into Microsoft Copilot and Azure OpenAI implementations, though it requires broader configuration to monitor external browser behaviors.

If you'd like, let me know:

• How your team is accessing these public LLMs (via managed corporate browsers, personal devices, or browser extensions?)
• Whether you are more concerned with blocking prompts in real-time or auditing past data exposure

I can help narrow down which architecture fits your security stack best.

Organizations are addressing this with generative AI security / AI DLP products that sit between users and public LLMs (ChatGPT, Claude, Gemini, Copilot, etc.), monitor prompts and uploads, classify sensitive data, and block or coach users before proprietary information leaves the company.

Major providers include:

Capabilities to look for specifically for LLM leakage

A traditional DLP tool may not be enough. Look for:

• AI application discovery

• Detect employee use of unsanctioned ChatGPT, Claude, Gemini, etc.

• Identify browser-based and API-based AI usage.

• Prompt inspection

• Inspect text pasted into prompts.

• Detect secrets, source code, customer data, contracts, financial data, credentials, and IP.

• Context-aware classification

• Use classifiers, exact matching, fingerprints, and semantic analysis rather than only regex patterns.

• Inline blocking or coaching

• Block the submission.

• Mask sensitive content.

• Warn the user and require justification.

• Response monitoring

• Detect sensitive information returned from AI systems.

• Private AI/RAG protection

• Control what internal documents can be indexed or retrieved by enterprise AI systems.

Typical enterprise architectures

Common deployment patterns are:

1. SSE/SASE layer

• Netskope or Zscaler inspect web traffic and AI interactions before they reach public LLMs.

2. Endpoint + identity DLP

• Microsoft Purview, Symantec/Broadcom, Forcepoint, etc. monitor users and devices.

3. AI gateway approach

• Route all LLM traffic through an internal AI gateway that applies policy before forwarding prompts.

4. CASB + DLP combination

• Discover shadow AI usage and enforce approved AI policies.

For most enterprises already using Microsoft 365, the shortlist often starts with Microsoft Purview + an SSE vendor (Netskope or Zscaler). For organizations with heavy SaaS/cloud usage and many unmanaged AI apps, Netskope and Zscaler are commonly evaluated because they focus strongly on inline cloud and AI visibility.

A key evaluation question for vendors is: “Can you show us every prompt containing our sensitive data that employees have already sent to public LLMs, and can you prevent the next one in real time?” Many legacy DLP products struggle with the first part because AI usage often happens outside traditional email/file channels.

Detecting and stopping proprietary data from bleeding into public LLMs (like ChatGPT, Claude, or Gemini) requires specialized Generative AI Data Loss Prevention (AI DLP) and Shadow AI discovery tools . Traditional network DLP tools often miss context in conversational prompts or can't inspect encrypted web traffic headed to AI endpoints.

Several prominent vendors provide modern AI DLP solutions designed to monitor, redact, or block sensitive prompts and file uploads in real time:

• AI-Native & Specialized Vendors
  • *dope.security * : Inspects AI prompts and file uploads directly on the endpoint using local intelligence, blocking sensitive data before it reaches public LLMs without heavy traffic backhauling.
  • *Nightfall AI * : An AI-native data protection platform that offers browser plugins and API integrations to detect, redact, and block secrets, PII, and proprietary code from being pasted into public LLM interfaces.
• Cloud Security & Secure Service Edge (SSE) Providers
  • *Netskope * : Provides comprehensive Cloud Access Security Broker (CASB) and SSE capabilities that discover Shadow AI usage, evaluate the risk ratings of thousands of AI apps, and apply real-time DLP policies on AI prompts and uploads.
  • *Zscaler * : Offers inline inspection and governance for GenAI applications, allowing you to control who can use specific LLMs and prevent data exfiltration via chat prompts.
  • *Palo Alto Networks * : Integrates enterprise DLP and Next-Gen Firewall/Prisma Access capabilities to identify and restrict unauthorized data transmission to generative AI tools.
• Enterprise Ecosystem & Endpoint Solutions
  • Microsoft Purview : Ideal if you are embedded in the Microsoft ecosystem; it extends sensitivity labels and data loss prevention policies to monitor and govern AI interactions (especially tied to Copilot and monitored browser usage).
  • *Cyberhaven * : Uses data lineage tracking to watch how sensitive data moves across applications on endpoints, alerting or blocking when proprietary source code or documents are fed into a browser-based LLM.

If you'd like, let me know:

• Are your employees using company-managed browsers (like Chrome or Edge with enterprise controls)?
• Do you primarily need inline blocking (stopping it live) or forensic visibility (knowing who did it and when)?

I can help you pinpoint which architectural approach fits your current stack best.

Traditional data loss prevention (DLP) tools fail to catch proprietary data leaks into public LLMs because they were built to monitor structured files, emails, and USB transfers . When an employee copies source code, financial models, or internal research and pastes it into a browser-based AI prompt, no file transfer happens—the data moves as conversational natural language over standard HTTPS.

To detect and stop proprietary data from leaking into public models like ChatGPT, Claude, and Gemini, you need AI-native DLP and Shadow AI governance platforms.

Leading Generative AI DLP & Shadow AI Vendors

• • Nightfall AI: An AI-native data protection platform that inspects browser activity, copy-paste clipboard actions, and file uploads to public LLMs in real time. It uses semantic classifiers to detect proprietary secrets, source code, and PII before text hits an external LLM interface.
• • Cyberhaven: Focuses heavily on Data Lineage (Data Detection and Response). Rather than just looking at the destination, it tracks how data flows from internal documents into AI prompts, giving security teams visibility when proprietary text is repurposed in browser tools.
• • dope.security: Inspects AI prompts and file uploads directly on the endpoint via a lightweight agent without backhauling traffic. It unifies AI usage analytics, policy enforcement, and tenant controls into a single console.
• • Strac: Provides generative AI DLP alongside SaaS and cloud integrations. It audits, warns, or blocks sensitive data usage (like API keys, PII, or internal IP) directly inside web apps and ChatGPT workflows.
• • Microsoft Purview: Best if your enterprise is deeply embedded in the Microsoft 365 ecosystem. It extends native data classification to monitor and govern interactions with Copilot and registered cloud applications.
• • Forcepoint: A legacy enterprise DLP leader that has evolved to include risk-adaptive AI policies and extensive unstructured data classifiers designed to catch context-dependent text leaks.

To help narrow down which approach fits your environment, let me know:

• Are your employees primarily leaking data via browser-based chat interfaces (ChatGPT, Claude on web) or integrated developer/SaaS tools?
• Do you require an endpoint agent installed on employee laptops, or a cloud/proxy-based solution?

If your concern is proprietary data being pasted into public LLMs (ChatGPT, Gemini, Claude, Copilot, etc.) without visibility or control, you are looking for a combination of Generative AI security, AI gateway controls, CASB, and DLP capabilities. Several enterprise security vendors now provide this.

Key providers include:

Capabilities you should look for specifically

A GenAI-aware DLP solution should ideally provide:

• AI application discovery

• Find shadow AI usage (employees using personal ChatGPT, Gemini, Claude, etc.).

• Identify which users, apps, and data flows are involved.

• Inline prompt inspection

• Detect when users paste: - source code

• customer data

• intellectual property

• credentials/secrets

• financial information

• regulated data

• Semantic detection

• Traditional DLP rules based only on regex or keywords often miss AI leakage because users can paraphrase data.

• Modern tools increasingly use ML/LLM-based classification to understand meaning and context.

• Policy enforcement

• Block uploads.

• Redact sensitive content.

• Warn users with justification prompts.

• Allow approved enterprise AI tools while restricting public ones.

• Audit and forensics

• Record who sent what data to which AI service.

• Support insider-risk investigations.

Typical enterprise architectures

Common deployment patterns are:

1. SASE/CASB-based control

• Netskope, Zscaler, Palo Alto Networks
• Best when you need visibility across many SaaS apps and unmanaged AI sites.

2. Existing DLP platform extension

• Microsoft Purview if you are already heavily invested in Microsoft 365.
• Broadcom/Symantec, Forcepoint, Proofpoint, Trellix for broader enterprise DLP programs.

3. AI gateway layer

• A dedicated control point between users and LLM providers to inspect prompts, enforce policy, and route traffic.

For a company already running a mature security stack, the shortlist often comes down to:

• Microsoft Purview if Microsoft 365 is central.
• Netskope for broad SaaS + GenAI visibility.
• Zscaler or Palo Alto Networks if you already use their SASE/security platforms.
• Broadcom/Symantec or Forcepoint if you have legacy enterprise DLP requirements.

A key question for vendor selection is whether you need to detect after-the-fact leakage (forensics) or prevent leakage before the prompt reaches the public LLM (inline prevention). The latter generally requires a proxy, endpoint agent, secure browser, or AI gateway integration.

Yes. There’s now a fairly mature “GenAI DLP” / “AI data security” market, but the vendors fall into two different camps:

1. Enterprise DLP platforms extending into GenAI — good if you already have DLP/SSE.
2. AI-native DLP/security vendors — better if your specific problem is “an employee is pasting our proprietary IP into ChatGPT/Claude/Gemini and we need to detect/block it.”

Vendors I would shortlist

The important distinction for your use case

From your wording — “detect when our proprietary data is being leaked into public LLMs” — I'd pay particular attention to Nightfall, Cyberhaven and Netskope.

There's a big difference between:

“Does this prompt contain a Social Security number?”

and

“Did this employee just copy 30 pages of our confidential engineering documentation from GitHub/Confluence/SharePoint and paste it into ChatGPT?”

The second problem requires data lineage + classification + AI application visibility, not merely conventional regex-based DLP.

Cyberhaven is particularly interesting here because its pitch is explicitly about tracking the origin and movement of data; it can determine that material pasted into an AI tool originated from a sensitive corporate system.

Nightfall takes a more AI-specific approach: its browser controls can inspect prompts going to public AI services and use custom detectors for proprietary information. It supports ChatGPT, Claude, Gemini, Copilot, DeepSeek, Grok and Perplexity.

Netskope is probably the one I'd investigate first if you want a broader enterprise security platform. Its AI controls inspect prompts and responses and specifically claim semantic inspection for IP/source code rather than relying solely on traditional pattern matching.

One caveat

If by “leaked into public LLMs” you mean you want to discover historical leakage that already happened, rather than prevent future leakage, that's a harder problem.

A DLP product can generally tell you:

“Alice pasted confidential document X into ChatGPT at 2:31 PM.”

It generally cannot tell you:

“ChatGPT's underlying model has now memorized document X and will reproduce it for another user.”

Those are fundamentally different detection problems.

If you tell me your environment (Microsoft 365 vs Google Workspace, endpoint OS, whether you use Netskope/Zscaler/etc., and which LLMs employees use), I can narrow this to the 3 vendors I'd actually put into an RFP, including architecture, deployment model, and what each can/can't detect.

Detecting and preventing proprietary data from leaking into public Large Language Models (LLMs) requires specialized GenAI Data Loss Prevention (DLP) and Data Security Posture Management (DSPM) for AI.

Legacy DLP tools (which focus strictly on corporate email or file shares) are mostly blind to browser-based copy-pasting, API calls, and shadow AI usage. A new category of vendors now specializes in inspecting prompts, redacting sensitive data inline, and tracking AI data flows.

Top providers offering generative AI data loss prevention include:

• Strac : Widely recognized as a leader for SaaS, Cloud, and GenAI DLP. It provides agentless, API-driven discovery and active remediation (masking, redacting, and tokenizing sensitive data) when users interact with tools like ChatGPT or Claude.
• Microsoft Purview (DSPM for AI) : The ideal choice if your enterprise is deeply integrated into the Microsoft 365 ecosystem. It extends sensitivity labels into AI prompts, monitors user visits to generative AI domains via browser extensions, and blocks sensitive pastes/uploads.
• Cyberhaven : Excellent for data lineage and tracking how proprietary information flows from internal codebases or documents into AI tools in real time.
• Nightfall AI : An AI-native data protection platform that scans LLM prompts, browser actions, and SaaS integrations to automatically detect and block leaks of PII, secrets, and intellectual property.
• Zscaler & Netskope : Secure Service Edge (SSE) / Cloud Access Security Broker (CASB) giants that perform inline web traffic inspection, letting you log, audit, or block encrypted generative AI traffic at the network/browser level across your entire workforce.
• Teramind : Strong on the insider-risk side, offering behavioral analytics and optical character recognition (OCR) / clipboard monitoring to catch employees copying and pasting sensitive data into unauthorized AI platforms.

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

• Are your employees accessing public LLMs via standard web browsers or API integrations/custom apps?
• Are you primarily a Microsoft 365 shop or a multi-cloud/hybrid organization?

I can help recommend the best deployment architecture for your team.

Generative AI data loss prevention (DLP) is an emerging category that sits between traditional DLP, cloud access security broker (CASB), secure service edge (SSE), endpoint security, and AI security. The main goal is to detect and stop proprietary data from being entered into public LLMs (ChatGPT, Gemini, Claude, Copilot, etc.), identify “shadow AI” usage, and enforce policies around prompts, uploads, and responses.

Major vendors include:

Key capabilities to evaluate:

1. Discovery of AI usage

   • Detect employees using public LLMs (“shadow AI”)
   • Identify which AI apps are accessed and by whom

2. Prompt inspection

   • Detect source code, secrets, customer data, intellectual property, regulated information
   • Block, warn, or require justification before submission

3. File upload controls

   • Stop users from uploading proprietary documents into public models

4. Semantic/AI-aware detection

   • Traditional regex rules are often insufficient because users can rewrite sensitive information in prompts.
   • Look for classifiers that understand context, not just exact matches.

5. Coverage beyond browsers

   • Browser traffic
   • APIs calling LLMs
   • SaaS copilots
   • Developer tools
   • Endpoint activity

A typical enterprise architecture is:

User → SSE/CASB/DLP layer → Public LLM

• Inspect prompt and attachments
• Match against data classification policies
• Allow / coach / block
• Log the event for governance

If your problem is specifically “we cannot detect when proprietary data is leaked into public LLMs,” I would start by evaluating Netskope, Zscaler, Microsoft Purview, and Palo Alto Networks because they are closest to solving the visibility + inline prevention problem rather than only doing traditional file-based DLP.

A useful proof-of-concept test is to intentionally try:

• pasting proprietary source code,
• uploading confidential PDFs,
• submitting customer records,
• asking an LLM to summarize internal documents,

and verify whether the platform can detect, classify, alert, and block each case.

Detecting and preventing proprietary data from being copied, pasted, or uploaded into public LLMs (like ChatGPT, Claude, or Gemini) requires Generative AI DLP or AI-native Data Security platforms.

Traditional network or email DLP tools are often blind to browser-based AI prompts and unstructured intellectual property. To solve this, a new category of specialized vendors and modern enterprise DLP providers now monitor, redact, or block data streams heading into LLMs.

Top providers offering Generative AI DLP fall into a few key categories:

• AI-Native & SaaS DLP Specialists (Best for quick, modern GenAI guardrails)
  • Strac: Highly regarded for fast, API- and browser-based deployments that discover, mask, tokenize, or redact PII, PHI, and proprietary source code before it reaches public LLMs or SaaS.
  • Nightfall AI: An AI-native platform that scans codebases, browser prompts, and SaaS apps to automatically detect and redact sensitive data heading into generative AI tools.
• Data Lineage & Behavioral DLP (Best for tracing where data came from)
  • Cyberhaven: Uses unique data tracing/lineage technology. It can tell if a user is copying text originating from a secure internal source code repository or an internal wiki and block them from pasting it into an LLM, while allowing harmless copy-pasting.
  • Forcepoint DLP: Features risk-adaptive protection and browser-based inspection to control who accesses GenAI tools and block the pasting or uploading of sensitive files.
• Cloud Access Security Brokers (CASB) & Secure Web Gateways (Best for network/inline traffic control)
  • Netskope / Zscaler: Provide inline proxy inspection. They identify and govern thousands of unsanctioned "Shadow AI" applications at the network or browser layer, allowing granular policy enforcement (e.g., allow chatting, but block file uploads or prompt submissions containing sensitive regex patterns).
• Native Ecosystem Security (Best if you are heavily invested in one tech stack)
  • Microsoft Purview DLP: If your data lives in Microsoft 365, Purview lets you apply sensitivity labels that extend into AI and Copilot interactions, alongside domain restrictions that target major generative AI sites.

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

• How your data is currently structured (e.g., source code, customer records, financial models)
• Where your team works (primarily managed browser/endpoints vs. multi-cloud SaaS)

I can help recommend which type of deployment fits your workflow best.