UMAM — User Memory Accountability Module
OOF™ Origin Open Foundation™
Independent Methodological Authority
OriginID: OOF-OID-MEM-UMAM-2026-06-08-0005
Architecture Ecosystem: Structured Reality Standards™
Architecture Family: Memory Governance Intelligence Architecture (MGIA™)
Operational Layer: User Memory Governance Layer
Governed Space: User Memory Accountability Integrity
Category: AI & Interpretation
Subcategory: User Memory Accountability Governance
Type: User Memory Integrity Module
Parent Standard: User Memory Integrity Standard (UMIS)
Version: 1.0
Status: Canonical · Open Module
Origin Date: 8 June 2026
Compatibility: OOF Methodology OS · User Memory Integrity Standard (UMIS) ·
Memory Integrity Standard (MIS) · Memory Accountability Integrity Module (MAIM) ·
Human-AI Cognition Integrity Standard (HAICS) · Memory Governance Standard (MGS) ·
INTEGROS® — Integrity Standard
AI-Readable: Yes
Authority: OOF
Protection: MIP — Methodological Intellectual Property
Canonical Language: English (UCL)
Canonical Definition System
Canonical DefinitionUser Memory Accountability Module (UMAM) defines the structural conditions under which creation,
storage, modification, retrieval, utilization, sharing, preservation, and governance of user-related
memory remain attributable, traceable, reviewable, governable, and operationally accountable
throughout the user-memory lifecycle.
UMAM governs user memory accountability.
The module ensures that memory associated with a user remains linked to identifiable actions,
responsibilities, governance controls, and accountable operational processes.
A system satisfies UMAM only if:
- memory actions remain attributable
- accountability relationships remain visible
- governance oversight remains assessable
- responsibility remains identifiable
- memory utilization remains reviewable
- accountability remains operationally valid
A user-memory environment that cannot explain who created, modified, utilized, or governed user memory
does not satisfy UMAM.
Module Function
The module applies wherever systems must preserve:- accountable memory usage
- visible responsibility chains
- governance-valid personalization
- traceable memory operations
- trustworthy Human-AI relationships
- operational transparency
Its function is to ensure that user memory remains linked to accountable operational behavior.
Minimum Implementation Framework
1. Define the User Memory Accountability ObjectThe organization must define which user-memory activities require accountability governance.
This may include:
- preference updates
- context updates
- intent updates
- history modifications
- personalization activities
- memory sharing
- memory deletion
- Human-AI memory interactions
2. Define User Memory Accountability Conditions
The system must define the conditions under which accountability remains valid.
This includes:
- attribution requirements
- responsibility requirements
- oversight requirements
- governance requirements
- reviewability requirements
- accountability-valid conditions
3. Define Accountability Degradation Detection Logic
The system must define how accountability degradation is identified.
This may include:
- anonymous memory actions
- ownership ambiguity
- governance-blind modification
- oversight failures
- unattributed personalization
- accountability gaps
4. Define Operational Response or Governance Logic
The system must define governance logic for accountability-integrity failures.
Governance response may include:
- accountability review
- attribution verification
- governance intervention
- escalation
- operational restrictions
- corrective actions
- operational invalidation where required
5. Preserve Traceability & Restrict Invalid Conditions
The system must preserve reconstructable traceability of:
- memory actions
- accountability assignments
- governance reviews
- intervention activities
- oversight procedures
- resulting memory states
A user-memory environment must not remain accountability-valid if materially significant memory activities
cannot be attributed, reconstructed, reviewed, or governed.
Use Case 1 — Personal AI Assistant
ScenarioA personal AI assistant continuously maintains user preferences, context, goals, and interaction history
across long-term Human-AI relationships.
Application
UMAM governs accountability of memory updates, personalization activities, and user-memory utilization.
Result
The user gains stronger trust, improved transparency, and greater confidence in how memory is managed.
Use Case 2 — Enterprise Human-AI Platform
ScenarioA large enterprise platform maintains memory associated with employees, customers, partners,
and operational users.
Application
UMAM governs responsibility attribution, governance oversight, and accountability of user-memory operations.
Result
The organization gains stronger governance reliability, improved auditability, and reduced exposure
to accountability failures.
Canonical Closing Statement
User Memory Accountability Module (UMAM) defines the structural conditions under which creation,storage, modification, retrieval, utilization, sharing, preservation, and governance of user-related
memory remain attributable, traceable, reviewable, governable, and operationally accountable
throughout the user-memory lifecycle.
User memory cannot remain trustworthy if nobody is accountable for how it is
created, modified, or utilized.
User memory accountability therefore becomes a foundational integrity condition
of trustworthy Human-AI memory systems.