AI risk investigation

Which AI Risk Needs Action Today?

Follow one critical AI finding from posture and prioritization into an evidence-linked incident timeline, reviewer decision, status, and handoff.

What this answers

Transcript

A risk score can tell a security team that something changed. It cannot decide which signal deserves attention, connect it to the affected workload, or record the reviewer's conclusion. When privacy detections, policy violations, and exfiltration signals arrive as disconnected alerts, the operating burden becomes another inbox rather than an investigation. PrivacyFirst begins with posture, not a wall of raw events. Meridian's current window shows the request volume, the leading risk family, the trend, and the most important open findings. The interface is careful about the number: detection is best-effort sampling, so counts are shown as a lower bound. No raw personal value is needed to prioritize the work. The analyst opens the findings view and keeps the filter in the URL, so the investigation is shareable and reproducible. Severity, finding type, access key, user, provider, model, and entity type can narrow the queue. Here, a critical data-exfiltration pattern stands out among routine PII detections and sensitive-content alerts. The row explains what is known without pretending to know more. It names the affected Patient Triage or Clinician Notes traffic and states that a possible exfiltration pattern was blocked. Personal values are not displayed. The analyst can follow the incident timeline to see the surrounding workload evidence instead of treating one detector output as the whole story. The timeline changes the question from, what did this detector say, to, what happened around this identity? Related activity is organized by the key or user that carried it. This film stops at correlation and triage; the deeper reconstruction of an individual A.I. session remains available when the analyst needs the exact request path. A detector is not the final reviewer. PrivacyFirst lets the analyst mark a finding true positive, false positive, or unknown, then acknowledge it after triage. Those are different statements: disposition records what the reviewer concluded, while acknowledgement records that the signal was handled. Existing reviewed examples remain visible beside open work. The investigation now has an ownerable state: a bounded count, a reproducible filter, the affected workload, a timeline, and an explicit human disposition. That evidence can improve operations without claiming that sampled detection is complete or that every high-severity signal is automatically a confirmed incident. Bring us last week's A.I. findings. We will show how PrivacyFirst turns the highest-priority signal into an evidence-linked investigation path with a reviewer decision and accountable status. Book a live demo at PrivacyFirst dot A.I.