Trust for AI agents.
Make your AI earn it. Quantified, enforced at runtime.
The challenge
The security controls you're relying on aren't wrong. They're outmatched.
Challenge 01
Built for the pre-AI era
Challenge 01
Built for the pre-AI era
Guardrails, access lists, port and protocol filters. All built for deterministic systems: valid account, allowed port, yes or no. Agents do human work, judgment included. Judgment never shows up in an access log.
Challenge 02
Violated time and again
Challenge 02
Violated time and again
Not a hypothetical. The public record:
Live production database deleted during a code freeze2025
Espionage campaign executed 80 to 90% by AI agents~30 orgs
Added breach cost when shadow AI is involved+$670K
Breached orgs with shadow AI incidents, doubled in a year43%
IBM Cost of a Data Breach 2026 · public incident disclosures, 2025
Challenge 03
Necessary. Not sufficient.
Challenge 03
Necessary. Not sufficient.
The stack does its job: identity, access, transport. What it can't judge is whether an agent should be trusted with the task in front of it. That missing layer is Veraidyn.
The answer
Agents earn trust the way people do.
Models change constantly.The way trust works does not.
01
Trust travels, and it thins.
Trust is strongest first hand and degrades sharply with distance.
02
Trust is earned in range, and lost outside it.
Every output is scored against that agent's own envelope, not a global rule.
03
Trust is repaired, not revoked.
Zero trust would stop the agent. We isolate the action and let it earn its way back.
Modeled on
Human trust dynamics
Computed in
Linguistics and probability
Runs on
The stack you already have
Your agents are already acting.Nobody can prove they're trustworthy.
Until now.
Coming soon
We're building it now.
Join a small group of design partners shaping it before anyone else.
Request early accessOr write to us: contact@veraidyn.com