AIER

AI Exposure Rating · v1.0

Published by AI Security Intelligence

The exposure rating of the AI economy.

AIER is the entity-level measure of how AI risk aggregates around an organization. A portfolio-grade reading — 0 to 100, tiered T1 (Minimal) through T5 (Critical) — held for every entity we’ve observed, published under the AI Exposure Standard (AIES) v1.0.

AIES v1.0 · open standard CC BY 4.0 · readings are citable Right of View · your reading is yours

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Helix Sample Health

Healthcare · Diagnostics

SAMPLE

T3 MATERIAL EXPOSURE

52 / 100 · peer 71%ile

EAE48External AI Exposure

IAE44Internal AI Exposure (Inferred)

TPE51Third-Party AI Exposure

AAE58Agentic AI Exposure

DPE67Data & Privacy AI Exposure

GVE45Governance Exposure

RGE62Regulatory Exposure

184 active signals · 3 quarantined · published under AIES v1.0

An AIER reading is a published statement.

Every reading is a composite of seven exposure dimensions, benchmarked against the entity’s sector cohort. Tiered T1 (Minimal) through T5 (Critical), so an executive audience can act on a single number and a research audience can drill into the substrate.

This is a sample. Yours is held.

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The Doctrine

Held for the entity. Published on claim.

Every entity in our observation set already has a reading. We hold it until the entity claims it — and then we publish it, under the standard.

Because a reading is an institutional statement about an organization. It carries governance implications, board-level implications, insurance implications. Institutional statements about an organization belong, first, to the organization itself.

We call this the Right of View. Enter your domain to claim the reading held for you.

Held for the entity Right of View Published on claim

The Substrate

Seven dimensions. One reading.

AIER composes an entity’s exposure from seven distinct dimensions, each a benchmarked 0–100 sub-score derived from public signals.

EAE

External AI Exposure

The observable AI surface the entity presents to the world — customer-facing agents, public APIs, disclosed models.

IAE

Internal AI Exposure (Inferred)

The AI footprint inside the organization, inferred from public signals — not from any private data or internal system.

TPE

Third-Party AI Exposure

Exposure inherited from the AI vendors, foundation-model providers, and integrations the entity relies on.

AAE

Agentic AI Exposure

Exposure through AI systems granted autonomous action or tool-use — the scope of what they can do.

DPE

Data & Privacy AI Exposure

Exposure through the operational data AI touches — sensitivity, sovereignty, third-party data sharing.

GVE

Governance Exposure

A gap measure — the maturity of AI oversight, disclosure posture, and voluntary commitments relative to deployment.

RGE

Regulatory Exposure

The density and severity of AI-relevant rules the entity operates under — sectoral concentration and active enforcement.

Full dimension methodology in the AIES v1.0 spec →

AIES v1.0

AI Exposure Standard

Version 1.0 of the AI Exposure Standard. Published July 2026 by AI Security Intelligence. The methodology that governs every AIER reading, versioned publicly and revised only.

Publisher: AI Security Intelligence
License: CC BY 4.0 · citable, quotable
Status: Published · open standard
Latest: v1.0 · July 2026
Next review: Rolling · via AIES CR log

Read AIES v1.0 ↓ Download the PDF

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AIES v1.0 · open standard CC BY 4.0 · readings are citable Right of View · your reading is yours