Solutions
Untrusted AI arrives three ways.
Most enterprises are exposed on at least one of these today, and usually do not know which. Start with the one that describes you.
Provenance
You are about to run a model nobody in your company has verified.
Open-weight and self-hosted models are absorbing real production workloads, and that decision is usually already made by the time security is asked about it. The provenance question behind it is still open.
What it costs you
01
You cannot inspect the training
Someone else chose the data, the objectives, and the alignment. In some cases under another jurisdiction’s requirements.
02
Benchmarks do not detect intent
A model with a trigger-conditioned backdoor scores exactly like a clean one, because the trigger is not in your test set.
03
The download is the supply chain
Fine-tunes, quantizations, and community re-uploads each add a step nobody attested. You inherit all of them.
Verify the model before it serves a request.
Vulnerability scanning fingerprints internal structure against an approved baseline and surfaces trigger-conditioned behavior that benchmarks cannot reach. It runs as a gate in your pipeline, so an unverified model does not reach production.
AI Model Vulnerability Scan →Runtime
Agents act with your credentials. Your stack only sees them talk.
Agents read email, query databases, call tools, and move across your systems at machine speed. They carry entitlements, but not endpoint discipline.
What it costs you
01
Compromise inherits every permission
A hijacked agent is not a leaked password. It is a credentialed insider that never sleeps and never hesitates.
02
Your controls watch the wrong layer
Request in, response out. The decision to call a tool or reach for a credential happens in between, where your stack has no visibility.
03
You cannot reconstruct what happened
No root cause, no forensics, and no evidence for the auditor who asks next quarter.
Oversight at the layer where agents actually decide.
Runtime oversight reads activations and the full decision-to-action chain, so a detection fires on what the agent was about to do rather than what it said. Containment happens in milliseconds, with the trace already assembled.
AI Detection & Response →Control
Your model vendor is writing your security policy.
Sovereign AI is not about where the servers sit. It is about who decides what your AI can do: you, or the vendor whose model you are calling.
What it costs you
01
Frontier model means
Frontier control
Frontier models refuse standard security tasks: SOC triage, threat-intel image analysis, routine document processing. That refusal is operational risk your team absorbs.
02
So teams strip guardrails
entirely
Security teams reach for guardrail-free open models just to do their jobs, trading someone else’s policy for no policy at all. That is the wrong trade.
03
Your evidence sits in someone
else’s system
When an auditor asks why a decision was made, the answer cannot be that the vendor decided.
Own the policy. Any model, anywhere.
Controls belong at the gateway, not inside the model. That is what lets you run an open model without running unguarded, and why the same measurement that classifies a request also produces the record your auditor reads.
AI Gateway →Why these are one problem
Three exposures. One measurement.
Provenance, runtime, and control are the same question asked at three moments: what is this model actually doing? Answer it once and you have covered all three.
Before it runs
Is this the model we approved?
The measurement compares internal structure to the baseline.
While it runs
Is it doing what we expect right now?
The same measurement shows intent before the action lands.
At the gateway
Whose policy applies?
And it is what your own rule fires on, with the record attached.
Three tools means three instrumentation points and three sets of evidence that never reconcile. One platform covers all three.
See the platformFind out which exposure you have.
The 4-minute diagnostic scores all three and tells you where to start.