# Information Ownership Register

**LPM reusable knowledge object v1.0**

A reusable template for assigning ownership, stewardship, review rules, evidence standards, and AI usage boundaries to critical enterprise information assets.

## Metadata
- **Object type:** LPM Knowledge Object
- **Primary LPM layers:** Information Ecology, Ownership Map, Governance Architecture, AI Amplification
- **Connected layers:** Decision Architecture, Communication Architecture, Platform Structure, Identity & Incentives
- **Primary use:** Assign accountable ownership to critical information assets, knowledge objects, records, metrics, policies, evidence, and AI-accessible content.
- **Website use:** Downloadable template, workshop guide, AI readiness resource, JSON object for Lapemo ingestion, and future guided skill.
- **Version:** 1.0
- **Owner:** LPM / Lapemo
- **Last reviewed:** 2026-06-24

## Purpose
The Information Ownership Register makes durable information accountable. It defines who owns the accuracy, lifecycle, access, evidence, and AI usage boundaries for critical documents, metrics, records, policies, data products, knowledge objects, prompts, and AI-readable assets.

In LPM terms, this strengthens Information Ecology by connecting information to ownership, decision rights, governance, communication, platform structure, and AI amplification.

## Core principles
- **Information without ownership decays:** Every durable information asset needs a named accountable business owner and a steward responsible for hygiene, review, and change control.
- **Ownership is not authorship:** The person who created a document, dashboard, or data product is not automatically accountable for its accuracy, usage, or lifecycle.
- **AI expands the blast radius:** Unowned documents, stale pages, weak metrics, and duplicate records become more dangerous when AI can retrieve, summarize, or act on them.
- **Stewardship needs operating rules:** Owners need review windows, evidence standards, access rules, quality thresholds, and escalation paths.
- **Truth must be maintained:** Important information should have a source, owner, status, version, confidence rating, and supersession rule.
- **Ownership must map to use:** The owner must understand who consumes the information, what decisions depend on it, and what risks emerge if it is wrong.
- **Retirement matters:** Old artifacts, abandoned dashboards, stale policies, and obsolete AI context should be archived or marked as superseded.

## Required fields
| Field | Definition | Required |
|---|---|---|
| Information asset ID | Unique identifier for the document, record, data object, dashboard, metric, policy, evidence set, knowledge object, prompt, model context, or AI-readable asset | Yes |
| Information asset name | Plain-language name of the asset being governed | Yes |
| Asset type | Document, dashboard, metric, data product, policy, control evidence, decision record, knowledge object, process guide, prompt, dataset, model output, or system record | Yes |
| Business domain | Function, product, region, capability, operating layer, or enterprise system category the asset supports | Yes |
| Primary LPM layer | The LPM layer most affected by the asset | Yes |
| Purpose / business use | What the information exists to support, explain, decide, measure, control, or automate | Yes |
| System / repository | Where the asset currently lives: SharePoint, Confluence, Jira, ServiceNow, BI tool, CRM, HRIS, data catalog, GRC, Lapemo, etc. | Yes |
| Source of truth link | Authoritative location for the current approved version or record | Yes |
| Accountable business owner | Role accountable for accuracy, relevance, use, lifecycle, and dispute resolution | Yes |
| Information steward | Role responsible for metadata, freshness, formatting, quality checks, review hygiene, and update coordination | Yes |
| Technical owner | Role responsible for platform, permissions, integrations, automation, and reliability when system-backed | Required when system-backed |
| Data owner / content owner | Role that governs definitions, data quality, content standards, and downstream usage | Required when applicable |
| Primary consumers | Roles, teams, workflows, dashboards, AI agents, vendors, controls, or decisions that depend on the asset | Yes |
| Decision dependency | Decisions, approvals, risk events, customer commitments, or operating reviews that rely on this asset | Required when material |
| AI access rule | Whether AI can retrieve, summarize, classify, recommend, update, or act on this asset | Yes |
| Human review rule | When a person must validate the asset before use, publication, decisioning, or AI action | Yes |
| Freshness window | How current the information must be to remain usable | Yes |
| Review cadence | Monthly, quarterly, semiannual, annual, event-triggered, or retired | Yes |
| Confidence state | High, medium, low, provisional, stale, disputed, superseded, or retired | Yes |
| Sensitivity / classification | Public, internal, confidential, restricted, regulated, customer-sensitive, employee-sensitive, legal-sensitive, or security-sensitive | Yes |
| Access rule | Who can view, edit, approve, export, automate, train on, or supersede the asset | Yes |
| Evidence standard | Proof required to treat the asset as decision-grade, audit-grade, or AI-usable | Yes |
| Lineage / dependencies | Upstream sources, linked records, dashboards, integrations, approvals, transformations, and downstream consumers | Yes |
| Exception path | Where conflicts, stale information, missing owners, or unauthorized use should escalate | Yes |
| Supersession rule | How prior versions are replaced, retired, archived, or marked obsolete | Yes |
| Last reviewed date | Date of last human review | Yes |
| Next review date | Date or trigger for next review | Yes |
| Change history | What changed, who approved it, and why | Yes |

## Information asset types
| Asset type | Examples | Typical owner roles | Failure risk |
|---|---|---|---|
| Strategic artifact | Strategy memo, roadmap, operating model, annual plan | Executive, transformation, product, finance | Outdated strategic context drives wrong prioritization |
| Decision record | Decision log entry, approval record, governance outcome | Decision owner, governance lead, portfolio leader | People reinterpret old decisions or ignore supersession |
| Metric / KPI | Dashboard, metric registry item, BI semantic definition | Business owner, data owner, BI steward | Competing definitions create false performance views |
| Policy / control | Policy, standard, control, exception, audit evidence | Control owner, risk owner, legal/compliance | Old controls get reused by AI or bypassed by teams |
| Knowledge object | LPM artifact, playbook, SOP, onboarding guide, framework asset | Artifact owner, steward, methodology owner | Stale guidance becomes institutional memory |
| Data product | Curated dataset, data contract, data mart, semantic layer | Data product owner, domain owner, platform owner | AI and dashboards rely on weak lineage |
| Process artifact | Process map, workflow guide, operating procedure | Process owner, function leader, system owner | Teams follow unofficial process variants |
| Customer / employee record | CRM record, HRIS profile, entitlement record, account file | Domain owner, data steward, system owner | Wrong routing, access, commitments, or personalization |
| AI context asset | Prompt, retrieval corpus, model card, evaluation set, agent memory | AI product owner, risk owner, content steward | AI produces confident output from unapproved context |
| Evidence pack | Source extract, audit proof, validation log, risk assessment | Evidence owner, control owner, reviewer | Decision or audit cannot be defended |

## Owner roles
| Role | Owns | Accountable for |
|---|---|---|
| Accountable business owner | Owns business accuracy, relevance, lifecycle, and risk acceptance | Approves use, resolves disputes, sets review expectations, accepts consequences if wrong |
| Information steward | Maintains asset hygiene and review discipline | Tracks metadata, freshness, links, formatting, versioning, and owner follow-up |
| Technical owner | Owns platform reliability, integrations, access, and automation boundaries | Manages repository, permissions, APIs, retention, and technical controls |
| Data / content owner | Owns definitions, data quality, content quality, and approved usage | Validates quality, resolves definition conflicts, reviews downstream dependencies |
| Risk / control owner | Owns policy, compliance, audit, and exception requirements | Defines evidence standard, control needs, review checkpoints, and escalation triggers |
| AI usage owner | Owns how AI is allowed to read, summarize, recommend, or act on the asset | Defines retrieval rules, human review rules, prohibited uses, and evaluation expectations |

## 500+ employee company version
- **Design intent:** Create visible ownership before knowledge becomes scattered across documents, chat, spreadsheets, and founder memory.
- **Minimum register:** Track critical documents, dashboards, decision records, policies, process guides, customer records, and AI initiative artifacts.
- **Biggest risk:** Useful information exists, but no one owns keeping it current, trusted, or safe for AI retrieval.
- **Ownership pattern:** One business owner and one steward per critical asset. Technical owner added when the asset lives in a managed system.
- **Review cadence:** Quarterly for core assets, monthly for AI-accessible or customer-impacting assets, event-triggered for major org or system changes.
- **AI focus:** Do not let AI retrieve from unowned repositories, stale playbooks, or duplicate documents without review and confidence labels.
- **Operating rule:** If an asset influences decisions, customers, employees, controls, or AI, it must be in the register.

## 5,000+ employee company version
- **Design intent:** Create cross-functional stewardship across functions, shared platforms, data domains, governance forums, and AI programs.
- **Minimum register:** Track enterprise knowledge objects, metric definitions, policies, controls, data products, process artifacts, decision records, and AI context assets.
- **Biggest risk:** Each function manages its own information standards, producing conflicting truths and inconsistent AI outputs.
- **Ownership pattern:** Business owner, steward, data/content owner, technical owner, and risk/control owner assigned by domain and asset class.
- **Review cadence:** Quarterly domain reviews, monthly AI/risk asset reviews, and automated stale-asset alerts through platform metadata.
- **AI focus:** AI retrieval and agent workflows must use registered assets with owner, freshness, confidence, sensitivity, and allowed-use metadata.
- **Operating rule:** High-use assets and AI-accessible assets require owner validation, source-of-truth mapping, and evidence standards.

## 10,000+ employee company version
- **Design intent:** Operate information ownership as an enterprise control layer across regions, business units, platforms, vendors, and AI agents.
- **Minimum register:** Maintain enterprise asset classes for data, knowledge, policy, control, metric, decision, model, agent, vendor, and regulated information.
- **Biggest risk:** AI scales stale or conflicting institutional knowledge across thousands of employees, systems, and automated workflows.
- **Ownership pattern:** Federated domain ownership with enterprise metadata standards, stewardship councils, data governance, model governance, and audit traceability.
- **Review cadence:** Risk-tiered review: monthly for regulated/AI/customer/employee-impacting assets, quarterly for operational assets, annual for stable reference assets.
- **AI focus:** AI systems must check ownership, classification, confidence, lineage, and review status before retrieving, summarizing, recommending, or acting.
- **Operating rule:** Unowned, stale, disputed, or superseded assets are blocked from decision-grade use and flagged for governance action.

## Confidence states
| State | Definition | Use rule |
|---|---|---|
| High | Owner confirmed, source mapped, review current, lineage visible, evidence standard met | Can support decisions and approved AI retrieval within defined boundaries |
| Medium | Owner known, source usable, minor gaps in lineage, evidence, or freshness | Can support operational use with caution and owner review for high-impact decisions |
| Low | Owner unclear, stale metadata, weak evidence, or unresolved definition issues | Cannot support critical decisions or AI action without review |
| Provisional | New asset not fully governed yet | Time-boxed use only; owner and review rules required |
| Stale | Freshness window missed | Flag for review; do not use for AI outputs or executive decisions until refreshed |
| Disputed | Conflicting owners, sources, definitions, or interpretations | Escalate through information governance or decision rights path |
| Superseded | Replaced by a newer source, artifact, metric, or policy | Archive or redirect users and AI to the current source |
| Retired | No longer active or approved for use | Remove from active workflows and AI retrieval scope |

## Workshop flow
- **1. Inventory:** List critical information assets by domain, system, workflow, decision, metric, policy, and AI use case.
- **2. Classify:** Assign asset type, sensitivity, business domain, LPM layer, and primary consumers.
- **3. Assign ownership:** Name the accountable business owner, steward, technical owner, data/content owner, and risk/control owner where applicable.
- **4. Map source and lineage:** Identify authoritative location, upstream dependencies, downstream consumers, and related decision/evidence objects.
- **5. Define AI rules:** Specify whether AI can retrieve, summarize, recommend, update, train on, or act on the asset.
- **6. Set review rules:** Define freshness window, review cadence, human validation trigger, and supersession rule.
- **7. Score confidence:** Rate ownership clarity, freshness, evidence, lineage, sensitivity, AI readiness, and lifecycle control.
- **8. Resolve gaps:** Create actions for missing owners, stale records, duplicate assets, unauthorized channels, or AI exposure issues.
- **9. Publish register:** Store the approved register in a durable system and link it to the source-of-truth map, decision log, and evidence checklist.
- **10. Operate as living object:** Review when systems, owners, policies, workflows, metrics, or AI use cases change.

## Validation rules
- **No owner assigned:** Asset is not decision-grade, audit-grade, or AI-usable until an accountable business owner is assigned.
- **No steward assigned:** Asset may exist, but hygiene and review accountability are incomplete.
- **No source-of-truth link:** Asset cannot be treated as authoritative.
- **Stale review date:** Asset must be flagged and removed from AI retrieval for critical use until refreshed.
- **AI access enabled without rule:** Block AI use until retrieval, summarization, update, and human review boundaries are defined.
- **Sensitive asset without access rule:** Escalate to technical owner, risk owner, or data governance.
- **Decision dependency without evidence standard:** Require evidence checklist completion before the asset supports critical decisions.
- **Duplicate asset found:** Assign one source of truth and mark duplicates as copies, references, or superseded assets.
- **Disputed owner or definition:** Escalate through decision rights model and information governance path.
- **Retired or superseded asset still in use:** Redirect users and AI to current source and log the supersession.

## Scoring model
| Dimension | Score | Question |
|---|---|---|
| Ownership clarity | 0-5 | Is there a named accountable business owner and steward? |
| Source clarity | 0-5 | Is the authoritative source location known and linked? |
| Freshness control | 0-5 | Does the asset have a review date, freshness window, and stale-state handling? |
| Evidence strength | 0-5 | Can the asset support decisions with traceable evidence? |
| Lineage visibility | 0-5 | Are upstream sources and downstream consumers known? |
| Access governance | 0-5 | Are sensitivity, permissions, export, and edit rules defined? |
| AI use readiness | 0-5 | Are AI retrieval, summarization, and action boundaries defined? |
| Lifecycle discipline | 0-5 | Is there a supersession, archive, retirement, and change-history rule? |

## AI prompts
- **Classify information asset:** Classify this asset by type, LPM layer, sensitivity, business domain, primary consumers, and likely owner roles.
- **Detect missing ownership:** Review this register and identify assets missing accountable owners, stewards, technical owners, or risk owners.
- **Score AI readiness:** Score each asset for AI retrieval readiness using ownership, source clarity, freshness, sensitivity, evidence, and human review rules.
- **Find duplicate truths:** Identify assets that appear to govern the same object, metric, policy, decision, or process and recommend one source-of-truth path.
- **Generate review actions:** Create prioritized actions for stale, disputed, unowned, sensitive, or AI-exposed assets.
- **Create source-of-truth mapping:** Map each information asset to its authoritative source, system role, owner, evidence standard, and AI usage rule.
- **Draft owner outreach:** Draft a short message asking an accountable owner to confirm accuracy, allowed use, freshness, and AI access rules for this asset.

## Example register
| ID | Asset | Type | Domain | Owner | Steward | Source | AI rule | Confidence |
|---|---|---|---|---|---|---|---|---|
| IOR-001 | AI sales enablement playbook | Knowledge object | Sales | VP Sales Ops | RevOps steward | Confluence | Summarize only; no autonomous send | Medium |
| IOR-002 | Customer health metric | Metric | Customer Success | Chief Customer Officer | BI steward | Metric registry | Allowed for insight; human validates action | High |
| IOR-003 | Employee access policy | Policy/control | IT / HR | CISO | Policy steward | GRC | Retrieve only; no policy interpretation without reviewer | High |
| IOR-004 | Product roadmap | Strategic artifact | Product | CPO | Portfolio steward | Portfolio tool | Summaries require owner-approved version | Medium |
| IOR-005 | Legacy process spreadsheet | Process artifact | Operations | Unknown | None | SharePoint | Blocked until owner assigned | Low |
| IOR-006 | AI agent prompt library | AI context asset | AI Platform | Head of AI Product | AI steward | Model registry | Use only in approved agent workflows | Provisional |

## How this becomes reusable
- Website artifact: publish the Markdown as the educational page and offer the DOCX and PDF as downloads.
- Workshop asset: use the DOCX to inventory information assets, assign ownership, and surface stale or unowned information.
- Lapemo ingestion object: convert completed rows into structured information objects tied to owners, source-of-truth records, decisions, evidence, systems, and AI workflows.
- AI skill: use the JSON schema and prompts to classify assets, detect missing owners, score AI readiness, and recommend governance actions.
- Living object: review and update when systems, owners, processes, policies, metrics, AI workflows, or business domains change.