It knows where the attacker is, and when they will move.
A deterministic kill-chain model, including the stages unique to AI serving, with a closed-form predictive horizon. Moves land before the attacker advances a stage, even against a zero-day that emits no alert.
A deterministic kill-chain model
Every workload is placed on an explicit attack kill-chain. No probabilistic guesswork in the decision path. The model is deterministic, auditable, and reproducible.
AI-specific stages
The kill-chain extends to attack stages unique to AI serving: model theft, prompt-injection persistence, and GPU side-channels. Pod-level tools never model these.
A closed-form predictive horizon
Phorvex projects how long before the attacker advances a stage, in closed form rather than by simulation, so the defense can move first.
No alert required.
Detection-first tools need a signature, an anomaly, or an analyst. A quiet attacker provides none of them. The Context Engine does not wait for an alert. It reasons about where an attacker must be on the kill-chain given the exposure that exists, then times each move to land before the next stage.
In our benchmark, that is the difference between eviction in about 5.5 seconds and a reactive baseline that never acted at all.
- Moves land before stage advance. The predictive horizon times each move ahead of the attacker's next step.
- 100% prevention on zero-day scenarios in the benchmark, where reactive-only tools scored 0%.
- Zero false positives. Preemption without paranoia, because moves are valued rather than triggered.
Watch the kill-chain model at work.
A technical briefing includes a stage-by-stage walkthrough against a live attack scenario.