PromptWake vs Zscaler AI Security
Zscaler governs what reaches AI services from your organisation. PromptWake keeps what AI services sent back into your codebase. The first is a gate; the second is a ledger — and a gate keeps no ledger.
you already run Zscaler and need AI application control, inline DLP and shadow-AI visibility across users.
you need to answer questions about AI-written code long after the traffic is gone.
| Capability | Zscaler AI Security | PromptWake |
|---|---|---|
| Model | Zero trust gateway between user and service | Local capture of what the tools wrote to disk |
| Decision moment | At transmission — allow, block, redact | After the fact — record and retain |
| Retention of content | Policy artefacts and incidents | The conversation itself, as the product |
| Ties to the repository | No | Yes — prompt, response, file diff |
| Works offline | No, by design | Yes — the free tier never leaves the machine |
| Scope | Every user in the organisation | Engineering machines |
If your organisation already routes traffic through Zscaler, its AI security capabilities are close to free in evaluation terms: application visibility, policy for which AI services are allowed, inline data-loss prevention on what users send, and reporting on the whole population rather than just engineers. Anyone worried about confidential data reaching a chat window should start there.
A gate makes decisions; it does not keep a record
The architecture is the argument. A zero-trust gateway exists to decide, in the moment, whether a transmission is permitted — and then to move on. It keeps what it needs for policy and incident purposes. It does not keep the AI conversation as an engineering artefact, because that has never been its job and retaining prompt content at that scale is a liability, not a feature.
So the question 'which prompt produced this function' has no answer in a gateway, even in principle, even with full inspection enabled. The gateway saw a request go out and a response come back. It did not see what the developer did with the response, which file it landed in, or whether they accepted it at all.
The gateway's question ends when the packet is allowed. The engineering question begins when the answer becomes a line of code.
One thing worth checking in your own deployment
Many AI coding tools are desktop clients talking to vendor APIs over pinned or non-browser paths, and coverage varies by tool, platform and how the endpoint is configured. Before assuming your gateway sees Cursor or Claude Code traffic at all, ask your security team to confirm it for those specific applications rather than for 'AI apps' as a category. The answer is often more partial than the dashboard suggests.
This is worth doing regardless of whether you ever look at us. An assumed control that does not actually apply to the tools your engineers use is the most expensive kind of coverage gap, because it comes with a report that says otherwise.
Where we fit alongside it
We do not block, inspect traffic, or cover anyone outside engineering — and we are not a substitute for a security service edge. What we add is the half that has no owner in most organisations: a durable, searchable record of what AI was asked and what it changed, kept locally by default and shared with the team on the paid tiers.
If someone has asked you to 'cover AI' and you already have Zscaler, the useful next question is not which product is better. It is which of the two questions — did data leave, and what did AI build — you currently cannot answer.