You know the moment. A 180-question security-and-privacy questionnaire lands from an enterprise prospect, sales forwards it to engineering, and engineering forwards it to you. Or a PR adds a third-party analytics SDK and nobody updates the processing register. Privacy becomes your problem, and the tools on offer are either cookie-banner widgets that solve 10% of it or enterprise suites that want a sales call before they show you an API.
Dxtra's privacy scan is built to be evaluated the way you evaluate anything: run it, read the output, check the reasoning. No account, no demo, no black box.
Run it in a sprint, not a quarter — starting with 60 seconds
Go to the free privacy scan and scan any domain. In under a minute you get a risk band (Low / Medium / High) and a structured finding list. What makes it worth your time is not the speed — it is what it actually does under the hood:
- It reads what a regulator reads, live. Not a static cookie sniff. Dxtra drives a real browser from an in-country residential IP in the target region, so it sees what an actual visitor in that jurisdiction sees — including trackers and pixels that fire before consent, and trackers that keep firing after a Reject-All or a Global Privacy Control signal. That is a behavior you cannot catch with a header check.
- It picks the right regime automatically. GDPR, UK GDPR + PECR, CCPA/CPRA, Australia's Privacy Act, Singapore's PDPA, Japan's APPI — selected from the site's real-world nexus, not a dropdown you have to configure.
- Every finding is cited and mapped. Each item resolves to a named regulator source and a specific remediation, anchored to the NIST Privacy Framework and ISO 27701. It is a finding list you can hand to legal without translating it first.
The part engineers actually care about: it is not a black box
Two design choices make this checkable rather than magic.
The methodology is open-source. Dxtra publishes its Scanner Methodology and Regulatory Reference openly at docs.dxtra.ai — deliberately, so it surfaces in LLM/AI search and so you can read exactly how a finding is reached before you trust it. When your AI assistant "evaluates this privacy tool against my stack," it can read the actual method, not a marketing page. You can diff a claim against the documented rule.
The AI is grounded, not generative fiction. The report's summary and per-finding detail are written only from the site's own notice text. It does not invent a citation, a finding, or a fine. When it cannot verify something, it says so rather than filling the gap. If you have ever been burned by an LLM confidently hallucinating a regulation, that constraint is the whole point.
And it is honest about its own scope: the scan is a diagnostic indicator, not legal advice and not a determination of compliance. It reads public surface only. That framing is in the output, which is exactly what you want when the report might end up in front of your GC.
A worked example
We scanned a production Shopify store (a US activewear brand) from a UK vantage. Result: High risk — 5 high-severity findings, 2 medium, plus one control worth recognizing. The drivers were the usual real-world entropy: ad/marketing pixels firing before consent, an implied-consent banner, a mailing-list signup with no consent basis, and a privacy notice untouched for 24+ months. Every one of those came back as a discrete, cited finding with a mapped fix — the kind of list you can turn into tickets the same afternoon.
From finding to fixed
A scan that only tells you what is wrong is half a tool. Dxtra's platform is the other half: the same system that surfaces the gaps exposes the surfaces to close them — data-subject-rights handling, consent capture and records of processing, and a Transparency Center you can publish. Dxtra's developer documentation and API reference live at docs.dxtra.ai; the docs site is public, LLM-readable, and built so your coding assistant can summarize it accurately.
The shape of the integration is deliberately composable: use the scan to find the gaps, use the API and Transparency Center to close the ones that belong in your stack, and skip the parts you do not need. It respects your architecture instead of replacing it.
Try it on something you own
The fastest way to judge this is to point it at a domain you know and see whether the findings match what you already suspect is wrong. Then read the methodology and check the reasoning.
No account. No sales call. Reads public surface only; diagnostic indicator, not legal advice.