CLEAR Assessment
A self-reported baseline
An initial view of governance maturity, potential gaps and priority areas. Self-reported — it establishes a baseline rather than independently verifying governance.
Take the AssessmentCLEAR · Our proprietary methodology
From governance that is documented to governance that can be demonstrated.
Visibility, accountability and evidence are what allow an AI decision to be explained and supported.
Organisations already have governance frameworks, policies and compliance programmes, and these remain essential. AI adds operational complexity: it becomes embedded in everyday workflows, influences decisions at scale and changes quickly. CLEAR is Yunique AI's proprietary methodology for translating the requirements an organisation is subject to into AI governance that operates, can be evidenced and is reviewed as AI use changes.
The premise
What documentation alone cannot demonstrate
Policies and frameworks establish expectations, and they remain a necessary foundation. AI widens the distance between a documented requirement and its operation: it embeds in everyday workflows, influences decisions at scale, is adopted outside central technology functions, changes rapidly, and involves several accountable functions at once. Each of those characteristics makes it harder to show that what the policy describes is what actually happened.
Requirements define what must be addressed. Operation is what has to be demonstrated.
Closing that distance calls for working mechanisms: visibility of AI use, defined ownership, controls that operate within existing processes, evidence generated as decisions are made rather than reconstructed afterwards, and review as AI use changes. CLEAR is the methodology Yunique AI uses to put those mechanisms in place alongside the frameworks and obligations an organisation is already subject to.
"If a decision can't be explained, it can't be defended."
Explanation is broader than model interpretability. It covers the AI use involved, the context, the information relied upon, who held authority, the risks considered, the controls applied, and the evidence supporting the decision — the things a regulator, auditor or court actually asks for.
The principles
Four principles that shape how CLEAR is applied
01
Visibility comes before assumption
AI use that is not visible cannot be governed, assessed or evidenced. The approved inventory is a starting point, not proof of the full environment — AI also enters an organisation through procurement, embedded product features and individual initiative.
CLEAR begins by establishing where AI influences decisions and where visibility gaps exist. Those gaps define where deeper discovery is warranted before further judgements are made.
02
Governance has to operate where decisions are made
Policies and frameworks establish expectations, and they remain necessary. The additional challenge with AI is demonstrating how those expectations operate at the point where AI is actually being used, by the people using it.
CLEAR connects governance to the processes where AI decisions actually happen — procurement, development and deployment, change, review and incident handling — so requirements are met through the way work is already done rather than through a parallel compliance exercise.
03
Explainability is the unit of trust
An AI-influenced decision may be questioned by a regulator, a customer, an auditor, a court or a board. Answering is rarely a matter of model interpretability alone: it means showing what AI use was involved, the information relied upon, who held authority, which risks were considered, which controls applied and where human judgement entered.
CLEAR treats that record as a standing requirement rather than something reconstructed afterwards, because evidence assembled after a challenge carries far less weight than evidence created as the decision was made. If a decision can't be explained, it can't be defended.
04
Accountability needs a name, not a gap
Accountability for AI is shared across legal, privacy, security, risk, technology and the business owning the outcome. The difficulty is not that distribution but the ambiguity within it: where decision rights are not explicit, responsibility is assumed rather than held.
CLEAR requires ownership, decision rights and accountability for AI use to be established explicitly, mapped to the organisation's existing governance structures rather than replacing them.
Documented expectations, demonstrable operation
Frameworks define what to address. CLEAR helps translate that into governance that operates and can be evidenced.
CLEAR works alongside them, focusing on whether the governance they describe operates in practice.
"If a decision can't be explained, it can't be defended."
What frameworks and policies define
- Requirements and expectations
- Roles and responsibilities
- Control objectives
- Periodic review
- What should happen
What CLEAR helps operationalise
- Visibility of where AI is actually used
- Named accountability for decisions
- Controls operating in everyday processes
- Evidence generated as the work happens
- What can be demonstrated and explained
What CLEAR is
A different operating model for AI governance
CLEAR is a methodology, not a standard. It does not replace regulation, legal obligations, ISO or NIST frameworks, or an organisation's own risk, privacy and security programmes — it serves a different function alongside them, defined by five operating principles.
Together they support legally defensible AI: governance, accountability and evidence that allow an organisation to explain and support its AI decisions under legal, regulatory, contractual, audit or board scrutiny. No methodology can guarantee an outcome; the objective is to be in a position to answer well.
Visibility-first
Governance begins with understanding actual AI use and identifying visibility gaps, rather than assuming the approved inventory represents the full environment.
Operationally driven
Connected to procurement, deployment, change, review and incident handling.
Continuous
AI use and risk change over time, so governance includes ongoing review rather than relying solely on a point-in-time assessment.
Accountable
Ownership and decision rights defined explicitly across the relevant functions.
Built to withstand scrutiny
Significant decisions, approvals, controls and reviews generate and retain evidence, so the organisation can explain how and why a decision was made.
How the methodology is applied
An initial baseline, then governance shaped to the organisation
CLEAR-based work often begins with the CLEAR Assessment, establishing a self-reported baseline against one question: how is AI currently governed, and where do visibility, accountability or control gaps warrant attention?
Governance is then shaped around the organisation's risk appetite, regulatory environment and existing frameworks. Organisations with more mature governance may begin at a later stage instead.
The intended outcome
- AI use is more visible and better understood
- Ownership and accountability are clearly established
- Governance and controls operate within relevant workflows
- Appropriate evidence supports significant decisions
- Governance adapts as AI use changes
- The organisation is better positioned to respond to scrutiny
Your first step
Start with the CLEAR Assessment
A self-reported diagnostic based on the CLEAR methodology, giving an initial view of governance maturity, potential gaps and priority areas. It is a structured way to understand your current position before committing to deeper work.
The Assessment establishes a baseline from what you report. It does not independently verify that governance operates, or prove legal defensibility — that is the role of the AI Defensibility Audit.
Take the CLEAR Assessment01
Identify potential visibility gaps
Highlights where AI use may sit outside current governance visibility, and identifies the areas that warrant deeper discovery.
02
Review alignment with obligations
Considers current governance practice against privacy, security and legal obligations, to indicate where attention may be needed.
03
See indicative priority areas
Shows which aspects of AI governance may warrant attention first — and where deeper, evidence-based verification would be worthwhile.
AI governance engagements
From a baseline position to governance that operates.
Four engagements, each with a distinct role: a self-reported baseline, advisory that turns it into priorities, independent verification of what can be demonstrated, and implementation. They are not a required sequence — organisations begin at the point that fits their existing governance maturity.
CLEAR Clarity Session
Strategic advisory session
Assessment results translated into a written 90-Day Priority Action Plan.
Explore the sessionAI Defensibility Audit
Evidence-based verification
Evidence-based verification of whether selected AI decisions, controls, ownership and supporting evidence can be demonstrated in practice, rather than described.
Explore the auditAI Governance Implementation
Implementation engagement delivered by Yunique AI
Governance, controls and operating models built into procurement, release and incident processes.
Explore implementationCLEAR · Common questions
What organisations ask about CLEAR.
CLEAR is Yunique AI's proprietary methodology for building AI governance that can be traced, evidenced and defended. It comprises five pillars: Clarity, Legal & Risk Alignment, Enablement, Accountability, and Responsible Execution. It translates governance, legal, privacy and security requirements into arrangements that operate in practice rather than existing only on paper.
No. CLEAR is a methodology, not a product. It is technology-independent, so it can be applied whatever AI systems, vendors or internal platforms an organisation uses.
It means governance an organisation can stand behind when it is challenged — by a regulator, auditor, court or customer. In practice that requires three things: a current view of where AI influences decisions, named accountability for those decisions, and retained evidence showing how and why each significant decision was made.
A policy states intent. CLEAR is concerned with whether that intent can be demonstrated. It focuses on embedding governance into the processes an organisation already runs — procurement, development, release and incident handling — and on generating the evidence that shows oversight actually happened.
No. CLEAR works alongside recognised standards and regulatory obligations. It is a way of meeting them in practice, and engagements can include gap analysis against AI management and regulatory requirements with a prioritised path to a defensible position.
Boards, executives and risk owners accountable for AI use — typically CISOs, DPOs, CPOs, CROs, General Counsel and the executives sponsoring AI adoption.
The first step is visibility
Understand where your AI governance currently stands
The CLEAR Assessment gives an initial, self-reported view of how AI is being governed and where visibility, accountability or control gaps may warrant further attention. Organisations already further along can discuss an engagement directly.