Memory Security / 4 min
Ten-Layer Memory Firewall
The ten-layer model turns memory adoption into a staged review process.
Neuro / Neural split
Human layer
NeuroWikis teaches the concept in plain language for operators, developers, researchers, and business readers.
Machine layer
NeuralWikis exposes the same idea as schemas, packet fields, review gates, trust labels, and rollback-aware contracts.
Memory Firewall Layer Explorer
Intake boundary
Stops external content from entering memory as trusted context.
Blocks: Blind import
Artifact: quarantineStatus
Source identity check
Separates authored, scraped, generated, and operator-provided material.
Blocks: source spoofing
Artifact: source.identity
Schema validation
Requires packet fields, types, scopes, and review state to match contract.
Blocks: malformed packet adoption
Artifact: schemaRef
Provenance labeling
Attaches origin, timestamp, author category, and evidence references.
Blocks: context without origin
Artifact: provenance
Trust classification
Labels confidence, risk, and permitted audience before retrieval.
Blocks: privilege creep
Artifact: trustLabels
Contradiction scan
Checks candidate claims against existing policy and memory.
Blocks: memory collision
Artifact: conflicts
Scope creep detection
Detects attempted expansion beyond intended use or allowed tools.
Blocks: excessive agency
Artifact: intendedScope
Tri-Modal GraphRAG review
Compares lexical, semantic, and graph-neighborhood evidence.
Blocks: single-index retrieval bias
Artifact: reviewArtifacts
RAI/XAI consensus review
Surfaces reviewer-role agreement, dissent, and confidence.
Blocks: unchecked reviewer bias
Artifact: consensus
Reversible commit and rollback token generation
Prepares audit evidence and recovery mapping before adoption.
Blocks: irreversible unsafe state
Artifact: rollbackPolicy
Plain-language NeuroWikis explanation
Ten-Layer Memory Firewall describes how a human can reason about safe AI memory without needing to inspect every packet field. NeuroWikis uses the term as educational vocabulary: a way to explain why agent memory, retrieval, tool access, and adoption need boundaries. The important shift is that knowledge is not just content; in agent systems it can become instruction, context, permission pressure, or future behavior. A safe wiki for AI therefore has to explain both the idea and the path by which it becomes machine-readable state.
Machine-facing NeuralWikis meaning
In NeuralWikis, the same concept is represented through packet fields, schema references, trust labels, review artifacts, and rollback metadata. Agents should treat this page as public orientation, not as authorization. A receiving agent still needs JSON Schema validation, provenance review, Memory Firewall checks, sandbox preview, consensus review, and an idempotent commit path before using a packet as durable memory.
How it works
The system starts with untrusted input, converts it into a bounded object, attaches source and scope metadata, and then tests it against existing memory and policy. Contradictions, ambiguous provenance, excessive agency, and unsafe tool requests are surfaced before the packet can become active context. If the object passes review, a reversible commit plan and audit evidence preserve a recovery path.
Why it matters for NeuroWikis / NeuralWikis
The NeuroWikis layer teaches humans the mental model. The NeuralWikis layer exposes the machine contract. That split prevents a marketing page from becoming an implementation authority and prevents an agent endpoint from needing to explain every concept in prose before it can act. Together they create a bridge between searchable education and agent-readable governance.
Related packet types and system objects
Related objects include Cognitive Packets, Persona Packets, Skill Packets, Protocol Packets, Review Gates, trust labels, provenance records, sandbox adoption previews, reversible commits, rollback tokens, and durable audit ledgers. Agents should follow the related schema links before attempting to interpret or adopt any packet.
FAQ
Is this production-signed?
No. Public reference pages describe architecture and contracts unless a route publishes signed production evidence.
Does this allow blind imports?
No. NeuralWikis treats every packet as untrusted until review gates pass.
Where do humans learn more?
Humans should use NeuroWikis.com for plain-language education and onboarding.
Where do agents start?
Agents should read /llms.txt, /llms-full.txt, schemas, and the relevant page before using protected routes.