Autonomous agents now hold real credentials and reach real systems. Lares watches every one, catches the rogue ones, prices the exposure, and breaks the chain, before an agent ever reaches your crown jewels.
One closed loop runs on every agent, continuously, from first sighting to a contained, reversible action.
Watch every agent at the gateway or from logs, and learn each one's authorized envelope of normal behavior. Unregistered agents surface as shadow AI.
Flag an agent as rogue only when multiple independent signals agree. One signal is noise; corroboration is a finding.
Fuse the deviation with your real environment (a concrete path to a crown jewel) and price the exposure in dollars.
Break the chain with an out-of-band trigger that cuts the one edge. Reversible, on a dial from watch-only to auto-contain.
Lares speaks in plain English and dollars, so the call is obvious, and the evidence is one click away.
Every finding leads with the bottom line (what an agent did and what it puts at risk) before any machine ID. Evidence-backed, never "validated."
Risk is expressed as a dollar range on the crown jewel in reach, so a 0–100 number never stands between you and the decision.
Start in observe, move to human-in-the-loop, arrive at auto-contain, per asset class, on your terms. Every action is reversible.
Lares corroborates before it calls anything rogue. A single anomaly is noise; independent signals pointing the same way is a finding worth a CISO's attention.
See the whole loop run on a live proving range, detect a rogue agent, price it, and break the chain in one click.