Shadow vs Netskope — open-source Netskope GenAI DLP alternative for AI traffic

Netskope One is a SASE platform with GenAI DLP: it sees which AI apps employees use, then blocks, coaches or redacts sensitive data inline. Shadow is an open-source privacy layer for AI API traffic: it swaps sensitive values for realistic, reversible fakes and restores them in responses, streams, tool calls and files. If you need company-wide visibility and policy for AI use, Netskope is built for that. If you need developer tools, agents and services to keep working while their prompts are masked, Shadow covers the gaps Netskope’s own docs list.

TL;DR

Netskope governs AI use across a managed fleet and can redact with mask, FPE or hash, but its docs say redaction does not apply to SSE streaming responses, MCP or file content. Shadow translates values into consistent fakes, restores them in streams and tool calls, and masks Office uploads in place. Many organisations would run both.

At a glance

	Shadow	Netskope One GenAI DLP
Open source / licence	Yes, Apache-2.0	No, commercial
Deployment	Endpoint HTTPS proxy, gateway (/gw/<provider>/), Rust library	SASE inline (cloud security service, steering client)
Protection method	Reversible realistic fakes from a persona; typed tokens for reports; optional redaction	Block, coach, allow; redact with mask, FPE or hash per DLP entity
Restores responses	Yes, automatically	Not documented
Streaming	Yes, SSE and NDJSON with split-fake reassembly	Redaction not applied to SSE streaming responses (per docs)
Tool calls	Yes, arguments restored locally; intent-aware for provider-hosted tools	Redaction not applied to MCP (per docs)
Files	DOCX, XLSX, PPTX masked in place; PDF to text; CSV, JSON, HTML	Upload controls; redaction not applied to file content (per docs)
Self-hosted	Yes, single binary, no account	No, delivered as a cloud service
Price model	Free	Quote

What Netskope does well

• Fleet-wide visibility. Netskope steers traffic from managed devices through its cloud, so security teams see which AI apps are used, by whom, and how often, including shadow AI that nobody approved. Shadow only sees traffic on machines where it is installed.
• One DLP policy for everything. The same DLP profiles, identifiers and exact-data-match rules apply to web, SaaS, email and AI apps. Teams that already tuned Netskope DLP get AI coverage without a second rule set.
• Coaching. Users can see a pop-up explaining why a prompt was blocked or what they should do instead, and can justify an exception. This changes behaviour, not just traffic.
• Inline redaction choices. Per DLP entity, Netskope can mask, apply format-preserving encryption or hash, which is more than most SASE products document.
• Central administration and reporting. Policy is set once in a console, incidents flow to the security operations workflow and SIEM, and reporting supports audits.
• Browser and web chat coverage. Employees using ChatGPT, Gemini or Copilot in the browser are covered without per-app configuration.

Where Shadow is different

Does Netskope work with streaming AI responses and agents?

Netskope’s redaction documentation lists exclusions: file content, SSE streaming responses and MCP traffic. Most coding assistants and agent frameworks stream responses and call tools, so those flows fall outside inline redaction. Shadow restores fakes in server-sent event streams, including fakes split across events, and restores tool-call arguments locally before your tools run.

Translate rather than block

Blocking breaks the tool, and users route around it. Shadow replaces jane@acme.com with hui.zhao@zhilan.cn from a persistent persona; card numbers stay Luhn-valid and IBANs pass mod-97. The model sees a coherent prompt and the answer comes back with real values. Netskope’s FPE preserves format too, but its docs do not describe restoring values in the response.

Intent-aware policy

Shadow classifies each request as act, report, code or converse and picks a treatment per value: a fake, a typed token such as [PERSON_0007], the real value for a message recipient or provider-hosted action target, or redaction. See intent-aware policy.

Files inside AI requests

Shadow masks DOCX, XLSX and PPTX uploads in place, extracts PDF text, and handles base64 and multipart payloads. See file masking.

Local, open source, free

Shadow is a single Rust binary with no account and no telemetry. Detection runs locally, so plaintext is never sent to a security cloud for inspection. You can audit the code.

When should you choose Netskope?

• You need to see and govern AI use across hundreds or thousands of managed devices.
• You want one DLP policy across web, SaaS and AI, with coaching and incident workflows.
• Your main risk is employees pasting into web chat UIs.
• You need central reporting for auditors and integration with your SOC.

When should you choose Shadow?

• Developers, agents and services call model APIs and rely on streaming and tool calls.
• You want answers to stay useful, with real values restored, rather than blocked or one-way redacted.
• You want Office files in AI requests masked before they leave.
• You need a self-hosted, open-source control that does not send plaintext to a third-party inspection cloud.
• You don’t have a SASE budget, or you need coverage for machines outside the managed fleet, such as CI runners and containers.

Using Netskope and Shadow together

The two tools sit at different layers and can coexist:

1. Keep Netskope for browser, SaaS and web-chat AI use on managed devices.
2. Install Shadow on developer machines, CI runners and servers that call model APIs, in proxy mode or gateway mode.
3. Decide which layer decrypts the AI API hosts Shadow intercepts. Either add those hosts to a Netskope SSL-inspection exception for the devices running Shadow, or point Shadow’s upstream_proxy at the Netskope path so it sees already-masked traffic.
4. Use Netskope reporting for fleet visibility, and Shadow’s per-request logs (counts and kinds, never values) for API traffic.

How do they compare on accuracy?

Netskope does not publish PII detection benchmarks on public datasets, and neither product can be measured fairly without a tenant. Shadow’s benchmark publishes its method, datasets and code so results can be reproduced.

Sources

• Netskope: Redact sensitive data
• Netskope One platform
• Shadow: intent-aware policy
• Shadow: security model

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Last reviewed: 2026-10-31. Netskope is a trademark of its owner; this comparison is based on public documentation as of the review date.

Frequently asked questions

Is Shadow a free alternative to Netskope?

For AI API traffic, partly. Shadow is free and open source (Apache-2.0) and masks prompts to AI providers with reversible fakes on the machines where it runs. Netskope One is a commercial SASE platform that covers web, cloud apps and AI apps across a whole managed fleet, with central policy and reporting. Shadow does not replace that breadth.

Can Netskope redact sensitive data in ChatGPT prompts?

Yes. Netskope's documentation describes inline redaction that can mask, format-preserving encrypt or hash data per DLP entity. The same documentation says redaction does not apply to file content, server-sent event (SSE) streaming responses or MCP traffic.

Does Netskope restore redacted values in the AI response?

Public documentation does not describe restoring redacted values in responses. Shadow restores its fakes in JSON responses, SSE streams and tool-call arguments on the local machine.

How much does Netskope cost?

Netskope does not publish list prices; pricing is by quote and depends on the modules licensed. Shadow is free to self-host.

Can Netskope and Shadow run together?

Yes. A common pattern is Netskope for employee browser and SaaS traffic on managed devices, and Shadow on developer machines, CI runners and services that call model APIs. Exclude the AI API hosts Shadow handles from Netskope TLS inspection, or chain the two, so certificates are not intercepted twice.