In plain English
This page explains where an AI behavior can live. It may be in a model, but it may also be in a prompt, memory record, adapter, dataset, tool setting, evaluator rule, or human workflow.
- Why this matters: AI risk can come from the whole arrangement, not one obvious model.
- What to look for: data, memory, routes, adapters, tools, evaluators, updates, and rollback paths.
- Technical version below: the expert terminology remains available and is linked through the glossary.
Persistent Memory
Persistent memory turns prior interactions into future context. That is useful for continuity and dangerous when poisoned, stale, over-trusted, or inherited by descendants without review.
What to record
Record the component owner, source, version, hash or identifier, permissions, load conditions, compatibility assumptions, and known failure modes. For memory and datasets, record retention, jurisdiction, provenanceA record of where a component or behavior came from. Open glossary definition, consent, and retirement procedures.
Persistence question
Ask whether the component can carry a pattern forward after the apparent original artifact is removed. If yes, it belongs inside the behavioral extinctionEvidence that a behavior is no longer expressible across active artifacts, descendants, memory, routes, compositions, and retained training material. Deleting one model is not sufficient evidence. Open glossary definition review.
Counterargument
A component can be benign and useful. The existence of a host does not imply harmful behavior. It only means the host belongs within the ecology-level safety boundary.