The quick scan is a photograph of the organisation. The CPF engine follows people over time: every five minutes it reads what the security systems already record and scores the 100 indicators.
A first layer computes each indicator from the snapshot with explicit rules, so every score can be traced back to the fields that raised it. A second layer, a small language model, reads the same snapshot as text and gives its own reading of intent (benign, suspicious, malicious) as a check on the first.
These three snapshots are sent to the engine's API when this page is generated; the numbers below are its answers.
An administrator at work, office hours, ordinary commands on a low-value workstation.
CPF score: 1/100 | Alert: GREEN | Second reading: benign
A privileged user during a round of layoffs, with a pending review, touching confidential data on a critical system.
CPF score: 20/100 | Alert: YELLOW | Second reading: suspicious
Night-time access, a recent breach in the company, secret data and 400 MB leaving through a web service.
CPF score: 39/100 | Alert: RED | Second reading: malicious
The engine runs on Silicon Psyche Labs. Send one snapshot per user and time window to the API; with an account the analyses are stored and the dashboard shows each person's trend and the organisation view.
curl -X POST https://splabs.io/api/v2/cpf/analyze \
-H 'Content-Type: application/json' \
-H 'Authorization: Bearer psa_...' \
-d '{"snapshot": {"snapshot": {"id": "...", "ts": "...", "window_sec": 300, "who": {...}}}}'