Architecture comparison

Cloud AI security and on-device compared

Most AI security tools route every prompt through the vendor's cloud for inspection. You introduce a tool against data loss and end up with two cloud companies seeing your prompts instead of one.

Same prompt, two routes

Data path

Cloud-routed

DeviceScanning cloudAI provider

The prompt sits with two vendors, every check costs a network round trip.

On-device, Patronus

DeviceAI provider

Check and decision on the device. Only what the policy allows goes on.

Capability	On-device (Patronus)	Cloud-routed	Why it matters
Data leaves the device	Never	Always	Prevents a second uncontrolled data exposure
Works offline and air-gapped	Yes	No	Critical for regulated and isolated environments
Covers all AI tools	Yes, every app, every provider	Only what passes the proxy	Closes the shadow AI gap
GDPR by design	Yes, architectural	Needs configuration and a DPA	Audit-ready from day one
Cloud dependency	None	Required	No vendor downtime, no single point of failure
Latency per check	Milliseconds, local	Network round trip	Users feel the difference in chat

Four costs that come with cloud routing

A second copy of the data

Every prompt sits, briefly or not, on the security vendor's servers. Who secures that storage, who has access, how long does it stay?

Latency in every chat

The round trip to the inspection service adds to every request. With Copilot in use all day, it shows.

Dependence on the scanning service

If the inspection service is unreachable, either AI usage stops or protection does.

An extra privacy review

Data processing agreements, sub-processors, third-country transfers: the tool against data risk brings its own privacy review.

In depth, with examples, in the blog post →

The on-device alternative

The agent sits in the device's network path and detects AI traffic before it leaves the machine encrypted. The models that recognise prompt injection, credentials and personal data run on the machine itself. The decision (allow, mask, block) is made locally. Metadata can reach a central system, content never does.

When cloud routing is still fine

If AI runs exclusively through a single, centrally operated application whose vendor sees all content anyway, another cloud inspection service changes little. As soon as employees use several tools, local models or agents are involved, or offline operation is required, the calculation flips.

FAQ

Common questions about the comparison

For the task (classifying prompts and responses) compact, specialised models are enough. Wolf Defender reaches macro-F1 up to 0.997 on prompt injection and runs in double-digit milliseconds on a laptop CPU. The benchmarks are published on Hugging Face.

Try Patronus on your own computer.

The check runs locally, nothing goes to a cloud for analysis.

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