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AI DLP buyer's checklist: 12 questions to ask every vendor

9 min read · Updated May 16, 2026

Every AI DLP vendor sounds the same on the homepage: “Stop sensitive data leaks to ChatGPT.” The differences only surface during a real evaluation - and by then you've usually already burned three weeks on a POC that won't answer the questions that actually matter. This is the checklist we wish every buyer had open in front of them on the first vendor call.

We've grouped the twelve questions into five categories: coverage, detection, operations, compliance, and commercial. Skip any that aren't relevant to your shop. The point is to make every vendor answer the same twelve questions so you can compare apples-to-apples.

Coverage: what surfaces does it actually protect?

1. Which AI tools are covered today - by SKU, not by roadmap?
ChatGPT, Claude, Gemini, and Copilot are table stakes. Ask about the tools your team actually uses: Cursor, Perplexity, Grok, the Anthropic API, internal RAG apps, and the long tail of shadow AI (Poe, Pi, You.com, custom Copilots). “On the roadmap” is not coverage. Get a written list of supported tools and the specific surfaces - browser, desktop app, mobile, API.
2. Does it cover unauthenticated and temporary chats?
ChatGPT supports temporary chats and the “anonymous” flow that doesn't require sign-in. Many network-only DLP tools only see signed-in traffic and miss these entirely. Same problem with personal Google accounts using Gemini. Ask specifically: “If a user opens ChatGPT in a non-corporate browser profile and pastes a customer email, will you see it?”
3. Browser extension, endpoint agent, or network proxy?
Each has tradeoffs. A browser extension is the lightest-touch deploy but misses native desktop apps (the ChatGPT macOS app, Claude desktop, Cursor) and IDE assistants. A network proxy catches everything on the corporate network but can't see traffic from personal hotspots or temporary chats it can't TLS-inspect. A device-level agent catches all of it, at the cost of a slightly heavier deploy. Pick deliberately; don't let a vendor pick for you with a hand-wave.

Detection: what does it actually catch?

4. What detection layers does it run?
Three are common: regex / deterministic patterns (good for credit cards, AWS keys, IFSC codes - cheap, fast, high-precision), named-entity recognition (good for names, addresses, organisations - medium cost), and contextual LLM detection (good for “is this paragraph an internal RFC” - high cost, high recall). Ask which stages run, in what order, and what happens when one stage flags but the next doesn't.
5. Where does detection physically happen?
If the redaction stage lives in the vendor's cloud, you've traded one third party (the AI provider) for two (the AI provider plus the DLP vendor). Ask: “Does the original, un-redacted prompt ever leave the user's device?” The answer should be no - or you should understand exactly which third party now sees your sensitive data and what their retention policy is.
6. Block, redact, or alert - which does it default to?
Pure blocking trains employees to route around the tool (personal phone, personal account, screenshot OCR). Alert-only is invisible to the user and lets the leak still happen. Redaction - replacing “Ravi Mehta” with [REDACTED_PERSONAL_INFO_1] - lets the work continue without the leak. Most teams want all three available with different policies per category. Make sure the vendor supports that, not just a global on/off switch.

Operations: can your team actually run it?

7. What does the audit log look like during an incident?
Ask for a real screenshot or export. The minimum useful set: timestamp, user identity, tool, surface (browser / desktop / API), categories triggered, action taken, and a redacted preview of the offending content. If the log only shows counts, you can't investigate.
8. What's the added latency on a typical prompt?
Anything over ~250 ms is noticeable; over ~500 ms and people will route around it. Ask for a real number, not a range. If the answer is “depends on the model tier,” ask for the p50 and p95 on the cheapest tier you'd realistically use.
9. How does it deploy and update at scale?
MDM channels (Intune, JAMF, Kandji, Group Policy) for the initial install. Auto-update with a kill-switch for new versions. Confirm the agent self-repairs broken TLS-interception state without an IT ticket - this is the single biggest source of “the AI tool stopped working” complaints.

Compliance: does it pass the audit?

10. Which regulations does it map to, and how?
The honest list depends on jurisdiction: DPDP Act (India), GDPR (EU), HIPAA (US healthcare), PCI DSS (cardholder data), SOC 2 (general). A good vendor can show you which categories in their detection map to which articles or controls - not just claim “HIPAA compliant” (which is meaningless without the BAA and the specific controls in scope).
11. Can data stay in-region or on-prem if you're regulated?
For Indian DPDP, healthcare, or financial-services buyers this is usually non-negotiable. Ask about: data residency for the vendor's cloud control plane, whether detection itself ever sends content to the vendor (Q5 again), and whether a self-hosted or in-VPC deployment is supported.

Commercial: can you actually buy it?

12. Published per-seat pricing - or "contact sales"?
Both models exist. Published per-seat means you can budget without three vendor calls; “contact sales” usually means custom enterprise contracts and 6-week procurement. Neither is wrong, but match it to your buying cycle. If you're a 50-person team and the vendor only sells to 500+ enterprises, you'll get deprioritised in support.

Bonus: things that aren't on this list (on purpose)

  • Detection accuracy percentages. Every vendor claims 95%+. Without a shared benchmark dataset, the number is meaningless. Run a POC on your own data instead.
  • Logo walls. Big customers prove enterprise-readiness, not that the product fits you. Ask for a reference customer of similar size and stack.
  • Gartner / Forrester placement. Useful signal, not a buying criterion. The Magic Quadrant lags the market by ~18 months.

Want to compare a specific vendor against this checklist? We've done the work for the eight most-evaluated AI DLP products. See all comparisons or jump straight to Zscaler AI Protect, Nightfall, Cyberhaven, or Microsoft Purview.

Sources & further reading