Methodology

The Talos AI Exposure
Intelligence Methodology

Understanding AI Exposure begins with understanding the relationship between AI adoption, organisational capability and the external AI environment in which every organisation operates.

The Problem We Are Solving

Most organisations cannot measure their AI Exposure

Boards and senior leaders can already measure many things that matter. Financial performance is tracked continuously. Cyber risk is assessed through established frameworks and reported at board level. Operational performance is monitored through KPIs and management information that is produced as a matter of routine.

Yet most organisations cannot answer a deceptively simple question: how exposed are we to AI, and how has that changed over time?

Not because the question is unimportant — it is increasingly central to how boards govern AI — but because no structured, repeatable methodology existed to answer it. Governance frameworks describe what organisations should do. They do not provide a way to measure what is actually happening and what it means.

Talos exists to address that gap. The AI Exposure Intelligence Methodology was developed to give organisations a structured, evidence-based and explainable way to understand their AI Exposure — and to track it as exposure changes over time.

Foundation

Built from first principles

The methodology was not derived from compliance checklists or adapted from existing governance frameworks. It was developed from first principles, beginning with a fundamental question: what actually determines how exposed an organisation is to AI?

Every concept introduced into the methodology is evaluated against five criteria. If a concept cannot meet all five, it is not included.

01
Fundamental
The concept must be genuinely foundational — not a proxy, a symptom or a policy artefact. It must describe something real about the nature of AI Exposure itself.
02
Independent
Each concept must contribute something distinct. If it can be explained entirely by another concept already in the model, it does not belong.
03
Measurable
The concept must be capable of structured assessment. Concepts that cannot be observed or evidenced in practice cannot be acted upon.
04
Explainable
A board member or senior leader with no technical background must be able to understand what the concept means and why it matters to their organisation.
05
Capable of validation
The concept must be testable against real-world data. As evidence accumulates, each element of the methodology should be refinable in response to what the evidence shows.
Structure

The exposure architecture

Organisational AI Exposure is not a single thing. It emerges from the interaction of six measurable forces — each independent, each fundamental, and each capable of contributing to or reducing overall exposure depending on how the organisation stands in relation to it.

We do not publish the weighting or scoring logic that determines how these forces combine. What we can describe is what each force represents and why it matters.

Force 01
AI Adoption & Footprint
The breadth, depth and pace at which AI is being used across the organisation — including tools that have been formally sanctioned and those that have not.
Force 02
Information Asset Exposure
The nature and sensitivity of the data and information assets that are flowing through AI systems — including client data, commercially sensitive information and personal data.
Force 03
Business Dependency
The degree to which critical business processes and decisions have become reliant on AI-generated outputs — and the resilience risk this creates if behaviour changes or outputs fail.
Force 04
Decision Influence
The extent to which AI shapes or informs decisions that carry commercial, regulatory or reputational weight — and the quality of human oversight applied to those decisions.
Force 05
Third-Party & Supply Chain Exposure
The AI-related exposure introduced through suppliers, platform providers and other third parties — including where AI is embedded in tools the organisation uses but does not directly control.
Force 06
Governance Control Strength
The effectiveness of the policies, oversight processes and controls that exist relative to the organisation's actual AI footprint — not what documentation says, but what evidence supports.
External Context

The AI environment

No organisation operates in isolation. AI Exposure is shaped not only by what an organisation does internally but also by the external AI environment in which it operates.

Competitors adopting AI at pace change the competitive landscape and the consequences of under-adoption. Suppliers embedding AI into the tools and services they provide introduce exposure that organisations do not directly control. Customers beginning to use AI in their own operations change expectations, workflows and the nature of client relationships. Regulators developing AI-specific requirements create a shifting compliance context that organisations must track and respond to.

The external AI environment forms part of overall AI Exposure. An organisation with modest internal AI adoption may carry significant exposure if the environment around it is moving rapidly and its posture leaves it under-prepared. The methodology accounts for this external dimension alongside the six internal forces.

Core Insight

Exposure is a relationship, not a score

Talos does not treat AI Exposure as a simple checklist to be completed or a threshold to be passed. Exposure is not determined by any single factor in isolation — it emerges from the relationships between the six forces, modified by the external AI environment.

Two organisations with identical levels of AI adoption can carry very different levels of exposure — depending on what data is involved, what decisions AI is influencing, how well controls are operating, and how prepared each organisation is relative to the environment they operate in.

This relational understanding is what separates AI Exposure measurement from a compliance checklist. A checklist can confirm that a policy exists. It cannot tell you whether that policy reflects your actual AI footprint, whether the controls described in it are operating effectively, or whether your exposure has increased since the last time you checked.

AI Exposure measurement requires structured assessment, organisational context and informed judgement — applied consistently, across the same Fundamental Exposure Forces, over time. That is what the methodology is designed to enable.

The AI Business Exposure Index™

The output of a Talos assessment is the AI Business Exposure Index — a board-ready view of where the organisation stands across all six Fundamental Exposure Forces and the external AI environment. It is structured for senior leaders rather than technical teams, designed to inform decisions rather than simply satisfy audit requirements, and updated over time so that boards can track whether exposure is increasing, decreasing or shifting across different parts of the organisation.

Evolution

A methodology that learns from evidence

The AI Exposure Intelligence Methodology is not a fixed checklist. It is reviewed and refined as evidence accumulates, so every organisation benefits from a methodology that improves as Talos learns from research, evidence and practical application.

That evidence comes from several sources:

Changes are made deliberately and conservatively. An organisation's exposure result should reflect real change in its own position — not casual changes in how exposure is measured. Talos is open about the fact that the methodology continues to develop: that is what makes it evidence-informed rather than fixed, and it is also why every update is treated with care.

Direction

Contributing to a new discipline

AI Exposure Intelligence is an emerging field. The frameworks, standards and shared vocabulary that will eventually define how organisations measure and communicate their AI Exposure are still being developed — by researchers, regulators, practitioners and standard-setting bodies across multiple jurisdictions.

Talos contributes to that development by doing the methodological work carefully, grounding it in evidence, and making the thinking available to the organisations and stakeholders who will shape how this field matures.

AI Exposure can be measured in a structured, evidence-based and useful way. A principled approach to assessing it produces genuinely useful intelligence for boards and senior leaders. Organisations that build that capability now will be better positioned as the field develops and expectations around AI governance continue to evolve.

We welcome engagement from organisations, researchers, policymakers and investors who share an interest in getting this right.

If you would like to understand how the Talos AI Exposure Intelligence Methodology applies to your organisation — or to discuss the methodology itself — we would be glad to talk.

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