In plain English
This page explains the governance layer: rules, logs, approvals, signatures, audits, permissions, and rollback tools. These controls are necessary, but they also become important failure points.
- 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.
Control
Control systems are the rules, logs, approvals, signatures, audits, and rollbackReturning a system to an earlier known state. Open glossary definition tools used to keep AI safe. But those controls also become important targets. If the control system is wrong, weak, or manipulated, the whole AI system can become unsafe.
The control plane is both guardrail and target.
External governance is necessary for adaptive ecologies. It also concentrates authority in the evaluator, registry, router, release controller, hidden tests, signing keys, and rollback machinery.
plane hidden testsevaluatorregistrysigning keysrouterrollbackhuman approvalmemory policy
The external control planeThe governance layer that decides what can run, change, access tools, or be released. Open glossary definition is necessary because candidates cannot be allowed to rewrite the rules that promote them. It is also a high-value target because it defines success, records evidence, signs releases, routes traffic, grants permissions, and performs rollback.
Cognivirus.com does not argue that governance is useless. It argues that governance is itself part of the system and must be minimal, separable, audited, versioned, reproducible, recoverable, transparent about assumptions, resistant to candidate influence, and resistant to organizational pressure.
Read the flagship page: The Control-Plane Paradox.
Added governance guides
- Adapter Reproduction Boundaries
- Mutualist Persistence Versus Parasitic Persistence
- UAI File Handoff for Model Ecologies
- Source Report Intake Governance
New control expansion
v1.8.0 report-driven pages
- Execution-Time Alignment Boundaries
- Fail-Closed Governance
- Reproduction Rate and Resource Limits
- Human Incentive Safety Boundary
- Cryptographic Provenance for Adapters
v1.21.4 edge-runtime controls
- Edge Runtime Reproduction Boundary — browser-side runtime, WASM, adapterA small add-on that changes or specializes model behavior. Open glossary definition, tokenizer, sampler, KV-cache, and local-storage controls.
- Zero-Dependency Browser LLM Architecture — report-derived architecture map for local model ecologiesA changing AI system made from many connected parts, not just one model. Open glossary definition.
v1.21.9 ModelBreeder governance
v1.22.1 decentralized persistence controls
- Decentralized Persistence — local AI ecology, handoff packets, cognitive-interface consent boundaryThe consent and control line around biometric, neural, attention, affective, or cognitive signals used by an AI system. Open glossary definition, and bounded local reset.
- Decentralized Persistence Review — worksheet for local runtime and reset evidence.
- Edge Runtime Reproduction Boundary — runtime-control companion to the decentralized persistenceA behavior-preservation risk that grows when AI state, memory, adapters, tools, and evaluators are spread across many local or independent systems. Open glossary definition surface.