EARM Asset Recognition, Measurement & Impairment
The Extended Asset Recognition Model (EARM) is the KRYOS framework that separates reportable data assets from operational data. It is designed to survive auditor, regulator, and red-team scrutiny.
A disciplined recognition framework that decides what counts as a data asset, on what basis, with which triggers - and what should be carried as operational data only.
Most organizations either overclaim - calling every dataset a strategic asset - or underclaim, leaving material data invisible. Neither posture survives audit, M&A, or capital diligence. There is no shared standard for what crosses the recognition threshold or how impairment should be triggered.
KRYOS applies EARM to every candidate asset surfaced by the Registry. We determine identifiability, control, future economic benefit, and reliable measurement; assign a measurement basis; codify impairment triggers; and explicitly document exclusions so they cannot resurface as surprise liabilities.
Data and assets reviewed
What the engagement delivers
Recognition criteria
Apply EARM thresholds for identifiability, control, future economic benefit, and reliable measurement, per asset.
Measurement bases
Select and document cost, fair value, or utility-in-use bases per asset class with a full audit trail.
Impairment triggers
Codify and monitor obsolescence, rights loss, demand collapse, and AI utility decay triggers.
Exclusion posture
Document why specific data sets are not recognized - preventing both overclaim and forgotten exposure during diligence.
Disclosure alignment
Align recognition decisions with current and emerging disclosure regimes and internal reporting policy.
Recognition challenge log
Every decision carries the challenges raised against it and the response - pre-empting hostile review.
Artifacts produced
Decisions you can defend
A defensible recognition position for every candidate data asset.
Measurement and impairment decisions that survive auditor and red-team challenge.
Clear separation between reportable assets and operational data - no silent overclaim.
The capital, audit, and AI consequences.
Recognition is upstream of every capital, audit, and disclosure conversation. Without an explicit recognition discipline, valuation is built on assumption, M&A diligence cracks, and AI investment is justified on narrative rather than measured asset value.
Questions clients ask before engaging.
Is EARM an accounting standard?+
EARM is a recognition and measurement framework engineered by KRYOS. It does not replace IFRS or US GAAP, but it produces decisions and evidence aligned with the recognition principles those standards apply.
What is the difference between recognition and valuation?+
Recognition decides whether an asset belongs on the books at all. Valuation determines what it is worth. KRDS valuation only runs against assets EARM has recognized.
What triggers an impairment under EARM?+
Documented triggers include obsolescence, rights loss, refresh failure, demand collapse, AI utility decay, and material litigation exposure. Each trigger is monitored and tied to a response.
Why explicitly document excluded assets?+
Undocumented exclusions resurface during diligence as surprises. Explicit exclusion lets KRYOS and counterparties see what was reviewed and why it is not carried as an asset.
Can EARM be applied to AI training data?+
Yes. EARM was extended specifically to handle AI-native assets such as training corpora, eval sets, RAG indexes, embeddings, and governance artifacts.
Begin with a forensic data asset assessment.
A focused engagement that maps your data estate, scores assets against KRYOS frameworks, and produces a board-ready brief on what to recognize, value, productize, and capitalize.