AI Security Insights

Architecture

Where AI security has to run: on-device versus cloud routing, traffic detection across modern protocols, and the trade-offs behind each choice.

9 articles

Sub-cent AI security inference: Why only 25% needs the deep model path

At its validated operating point, Patronus Ark sends only about 25% of chunks to deep L3 inference while remaining approximately at full-L3 quality.

Architecture · Sep 19, 2026 · 4 min · Read →

How NTDBs Make Decisions Without a Transformer

How Patronus Ark uses shared embeddings and a fusion of inexpensive classifiers to make Layer-2 security decisions without running a transformer.

Architecture · Sep 12, 2026 · 10 min · Read →

AI DLP vs. classic DLP: why your DLP can't see prompts to local models

The most common sentence in conversations with security teams is: we have DLP, but it doesn't see the prompts. That is not a configuration error. It is an architecture question.

Architecture · Sep 7, 2026 · 7 min · Read →

AI security vendors in the DACH region 2026: who does what

Search for AI security in the DACH region and you get a list of platforms, governance tools, model providers and a handful of actual security products. This overview sorts them by what they really do.

Architecture · Sep 7, 2026 · 9 min · Read →

Langdock vs. Patronus Protect: AI workspace or AI firewall?

Both products come up in the same conversations but solve different problems. Langdock gives employees a safe place for AI. Patronus shows whether they use it, and protects every other path.

Architecture · Sep 7, 2026 · 8 min · Read →

How We Run Real-Time AI Security Inference On-Device on a CPU

Patronus Ark runs layered AI security inference on an ordinary laptop CPU at roughly 750 MB peak RSS and around 500 milliseconds median latency.

Architecture · Aug 31, 2026 · 14 min · Read →

How Patronus Detects AI Traffic Across WebSockets, SSE, gRPC, Protobuf and Modern Encodings

How modern AI traffic increasingly moves beyond simple JSON APIs into WebSockets, SSE, gRPC, Protobuf and binary streaming protocols, and how Patronus transparently detects and analyzes AI interactions across these transport and encoding layers while intentionally separating AI detection from semantic extraction.

Architecture · May 17, 2026 · 7 min · Read →

Cloud vs. On-Device AI Security

Most AI security tools route every prompt through the vendor's cloud to inspect it. You added a tool to reduce data exposure and ended up with two cloud companies holding your prompts instead of one. Here is what that costs and what the alternative looks like.

Architecture · Apr 2, 2026 · 8 min · Read →

Patronus Protect - On-Device Protection for your AI Interactions

Architecture · Jan 18, 2026 · 7 min · Read →

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