Strac alternative: broad DLP, or masking focused on AI traffic

Buying a DLP tool usually starts with one incident: a customer list in Slack, a contract in a shared drive, or a pasted ID in ChatGPT. What you buy depends on whether that was one instance of a wide problem, or whether AI is the specific thing keeping you up at night.

Maskflare wrote this page. Strac and Maskflare both redact prompts, so the difference here isn't masking against blocking. It's scope and depth. Strac's details below are its own claims.

What is Strac?

Strac is a data security vendor combining DLP and DSPM across SaaS, email, endpoints, cloud and GenAI. Its GenAI DLP page describes an agentless approach with more than 1,700 policies and classifiers. Its ChatGPT DLP page lists modes of audit, alert, block and redact, and also mentions pseudonymization, which replaces values with fake data.

For AI use, it offers a browser extension for Chrome, Edge, Safari and Firefox, an endpoint agent for Mac and Windows that includes the ChatGPT desktop app, and a Shadow AI dashboard to approve, observe or block tools. It also describes MCP DLP that redacts sensitive data on agent tool calls, and OCR on files and screenshots. We couldn't find published DLP pricing, whether originals can be restored in answers, or where its product is hosted.

Strac's blog describes inspecting each prompt and redacting or blocking PII, PHI, source code and secrets. That's a real capability, so don't let anyone tell you only Maskflare removes values from prompts. The better questions are whether the values come back in the answer, whether the model sees a token or fake data, and what's recorded along the way.

Who should choose Strac?

• Your problem is wider than AI. Slack, Drive, email, tickets and cloud data all need scanning, and AI is one channel among them. Maskflare doesn't scan SaaS data at rest.
• You want one DLP console. Fewer tools and fewer contracts, with AI as one of the policies.
• You also need data discovery. DSPM finds sensitive data where it already sits, which is outside Maskflare's scope.
• A redacted prompt is good enough. If people don't need the values back, you don't need tokens that restore.
• Pseudonymized text suits your workflow. Fake but realistic values can read more naturally to a model. Maskflare uses tokens, partial masking, hashes and format-preserving encryption, not synthetic data.

Who should choose Maskflare?

1. AI traffic is the actual problem. Maskflare is built only for this: apps, people and agents calling AI models and tools.
2. You need complete answers. Tokens are restored where the answer repeats them, including in the browser and while streaming. A draft comes back with the real name in it.
3. You build on AI providers. Change one base URL, and calls to OpenAI, Anthropic, Google Gemini, Mistral, xAI, DeepSeek, Groq, Together AI and Ollama go through the same rules. RAG and embeddings keep working, because the same value gets the same token.
4. Your coding agents touch real data. Hooks for Claude Code, OpenAI Codex, Cursor, GitHub Copilot CLI and VS Code agent mode block prompts containing secrets and mask tool input and output.
5. You need Linux or EU hosting. The desktop agent runs on Windows, macOS and Linux, and Maskflare is EU-hosted by default, with on-premises for enterprise.
6. You want every match explained. 574 detectors, 47 country packs and 232 credential formats, with a playground that shows why a value was or wasn't masked.

Strac vs Maskflare, feature by feature

What's the practical difference between redacting and restoring?

Take a support agent asking AI to write a refund email for a customer with an IBAN on file. With redaction, the model writes around a gap, and the agent fills it in by hand. With pseudonymization, the model sees a fake name, and the output contains that fake name unless something maps it back. With reversible masking, the model sees a token, and the agent gets an email with the real name and IBAN in place.

That gap matters more the more output you produce. For one analytics question it's nothing. For hundreds of customer letters a day, it's the difference between a tool people keep using and one they route around.

How do you move from Strac to Maskflare, or add it?

1. Separate AI policies from the rest. Keep SaaS and email DLP where they are, and take only the GenAI policies across. You may not need to replace anything else.
2. Map data types. For each Strac policy, find the detector or compliance profile. Add dictionaries, custom regex or Exact Data Match for your own terms.
3. Decide the action for each type. Mask for everyday values, block for credentials, redact where nobody needs the value back.
4. Remove overlap in the browser. Two tools acting on one prompt confuse people and results. Turn off AI redaction for the pilot group.
5. Pilot in alert mode, then switch rules to mask, and connect your SIEM.

What Maskflare doesn't do

• It doesn't scan SaaS apps, email or cloud storage at rest, and it isn't a DSPM.
• It doesn't produce synthetic replacement values.
• AI app discovery and coaching dialogs need the desktop agent on managed devices; personal phones and unmanaged laptops are out of reach.
• Prompt-injection detection runs on MCP tool results and tool listings, not on chat prompts, and there's no topic filtering.
• SOC 2 Type II and ISO 27001 audits are in progress. If you need a finished report today, factor that in.

Sources and how we checked

Based on public information as of November 3, 2026. Maskflare wrote this page, and vendors change their products, so check anything that matters with the vendor. “Not publicly documented” means we couldn't find it, not that the product lacks it. If something here is wrong, tell us and we'll correct it.

• strac.io/integrations/generative-ai-dlp
• strac.io/integrations/chatgpt-dlp
• strac.io/blog/how-to-block-chatgpt