Learning loop
EVERY VERDICT MAKESTHE NEXT ONE SHARPER.
Agentiks maintains an in-house verdict model that retrains continuously on signals from every layer. New attack patterns observed anywhere in the fleet get encoded into the platform within hours.
The loop
Four turns, continuously.
- 01
Every verdict is a signal
When a layer accepts, flags, or quarantines a sample, that decision — along with the detector scores that drove it — becomes training data for the verdict model.
- 02
Verdicts compose
The model learns which combinations of detector scores predict real poisoning, across the population of all samples the stack has seen. Weak correlations across layers combine into strong verdicts.
- 03
Retrain continuously
The verdict model retrains on a rolling window. New attack patterns — observed at one partner, one pipeline — propagate into the shared model. Every integration makes every integration stronger.
- 04
Ship hourly, not quarterly
When a new poisoning technique is seen in the wild, the detection signal is live across the fleet within hours. No vendor release cycle, no pipeline redeploy.
Why it matters
Poisoning evolves. The defense has to.
A static rule-set for ML data integrity is obsolete the moment it ships. Attackers iterate. The defense needs to iterate faster — which is only possible if the platform itself is learning.
Your data stays yours
The shared model learns from signals (verdict scores, detector outputs), not content. No raw samples leave the tenant boundary.
Opt-in federation
Partners choose what signals to contribute. Opt-in by layer, by sensitivity, by source. The shared model works with partial participation.
Versioned, explainable
Every verdict model is versioned. Every decision cites which model produced it. Rollback is a first-class operation.