PromptWake vs Netskope One AI Security
Netskope can tell you which AI tools your people use and stop sensitive data reaching them. It cannot tell you what those tools wrote into your codebase — the traffic is encrypted to a vendor and the record is a file on a laptop.
you need shadow-AI discovery and inline data-loss prevention across the organisation's traffic.
you need the engineering record of what AI was asked and what it changed.
| Capability | Netskope One AI Security | PromptWake |
|---|---|---|
| Vantage point | The network — a security service edge | The developer's machine |
| Best answer it gives | Which AI apps are in use, and what data reached them | What was asked, answered and changed |
| Can block | Yes, inline | No |
| Sees AI coding tool sessions | That they happened, not what they produced | The full session and the resulting diff |
| Covers non-engineering staff | Yes, the whole organisation | No, engineering only |
| Buyer | Security | Engineering leadership |
Netskope's AI security sits where all the traffic is: a security service edge that sees which applications people reach, classifies what is being sent, applies policy, and can block or coach in real time. For the shadow-AI problem — which of the hundreds of AI tools on the internet are our employees actually using, and what are they pasting into them — that vantage point is the right one, and no endpoint tool matches it.
What the network can and cannot resolve
From the network, an AI coding session is a TLS connection to a vendor's API. With inspection in place you can see that it happened, how large it was, and — for supported applications — apply data-loss policy to the content. What you cannot get is the thing an engineering organisation needs: the conversation as a durable, searchable record tied to the lines of code it produced.
That is not a limitation of Netskope's implementation. It is what the vantage point is for. Security tooling exists to make a decision at the moment of transmission — allow, block, redact, alert — and having made it, it has no reason to keep the payload as an engineering artefact, and generally very good reasons not to.
A DLP decision is made in milliseconds and then the traffic is gone. A provenance question is asked eleven months later, about one specific change, by someone who was not there.
The overlap that causes the confusion
Both products can produce a sentence beginning 'here is what your developers did with AI'. The sentences finish differently. Netskope's finishes with which applications were used and whether anything sensitive left. Ours finishes with which prompt produced which diff on which branch.
In practice these are bought by different people from different budgets, and the collision happens when a CISO is asked to 'cover AI' and reaches for the platform already deployed. It covers the half that platform was built for. The engineering half remains uncovered, and usually nobody notices until an audit question arrives that the security stack has no data to answer.
How to sequence them
If the worst outcome you can imagine is customer data leaving through a chat window, start with the network — Netskope, Zscaler or whatever your SSE already is. That is the risk they were designed for and we do nothing about it.
If the worst outcome is an incident nobody can explain because the conversation that produced the code was deleted after thirty days, no amount of network inspection reaches it. That record lives in files on developer machines, and reading them is a different product.