AI Governance · United Kingdom

Everyone is using AI.
Nobody is measuring how.

Validara inventories the AI tools running inside your business and scores each one against six risk domains — so you can hand a customer, insurer, or regulator a defensible answer to a question they're starting to ask: how do you actually govern the AI you use?

Validara measures how well the AI inside a business is governed — turning invisible, unmanaged AI use into an evidence-based score any customer, insurer, or regulator can trust.

Built on 15+ years in quality engineering

Validara is led by a quality-assurance leader with over 15 years testing and signing off software before it ships — owning test strategy and leading QA teams. That discipline is the whole idea: governance you can evidence, not governance you're asked to take on trust.

Standing & method

What Validara stands on

Every claim on this page is one you can check — the method is published, the credentials are real, and the limitations are stated. The same standard Validara holds your AI to.

15+ years of quality-engineering leadership

Owning test strategy, leading and mentoring QA teams, signing software off before it ships. Assessment built by someone whose career was proving things work — not asserting they do.

Independent by design

No vendor partnerships, no reselling, no referral commission. Validara has no product to sell you off the back of the findings — which is precisely what makes the findings worth reading.

Published methodology, not a black box

Six risk domains, weighted and reasoned in the open. The scoring model and its limitations are set out in the whitepaper — read the method before you buy the assessment.

Read the whitepaper (PDF) →

An established, insured practice

Operating through an existing UK registered company carrying professional indemnity and liability cover — the paperwork a procurement team asks for before an engagement, already in place.

Structured toward recognised standards

The framework is mapped toward ISO/IEC 42001 and the NIST AI RMF, and aligned to current UK obligations. Building toward that alignment — not claiming an accreditation Validara doesn't hold.

The implementation gap

You know what AI you pay for. You can't evidence how it's controlled.

AI enters a business bottom-up — individual staff, free tiers, personal accounts — faster than any policy can track. That gap is invisible until something goes wrong, or until someone asks you to prove otherwise.

Shadow AI

Tools you don't know about

Staff adopt AI tools without approval, oversight, or a record. Most businesses can't produce a complete list of the AI in use across their teams.

No owner

Nobody accountable

No named owner, no acceptable-use policy, no approval trail. When a regulator or customer asks "who's responsible for this?", there's no answer.

No evidence

Claims, not proof

Ticking "yes, we have a policy" convinces no one. What carries weight is evidence — the policy itself, the access logs, the review records.

How it works

An AI register that stays current — not a one-off audit

Most assessments are a report that's out of date within a quarter. Validara is a living register: assessed, signed off by a human, and kept honest as your AI use changes.

01

Discover

We find the AI actually in use — including shadow tools hiding in expenses, browser extensions, and features buried inside everyday software.

02

Assess

Each tool is scored against six risk domains. Every "in place" answer is expected to be backed by evidence, not just claimed.

03

Verify

AI does a first pass to speed things up; a human assessor confirms or overrides every judgement before anything is finalised.

04

Report

A weighted score, a tier, a prioritised remediation list, and a register you can hand to any customer, insurer, or auditor.

05

Keep current

Tools flag for re-review as they age. New tools get added, retired ones get closed off properly — so the register never goes stale.

What we measure

Six risk domains, weighted by consequence

Not an even average. Data Exposure carries the most weight because it maps to the highest-consequence, most-regulated failure modes.

DAT22%

Data Exposure

What data enters AI tools, where it flows, how long it's kept.

GOV18%

Governance

Named owner, acceptable-use policy, approval before deployment.

ACC16%

Access & Permissions

Role-based access, no shared logins, periodic review.

MON15%

Monitoring & Oversight

Human review of outputs, incident path, log retention.

CST15%

Cost & Spend Visibility

Spend monitored, caps in place, ongoing value reviewed.

DEP14%

Operational Dependency

Fallback if unavailable, SLA known, exit plan in place.

AI-assisted, human-verified

The machine drafts. The assessor decides.

Validara uses AI inside its own workflow — to read your policies and draft a first-pass score in minutes. But an unverified machine judgement is exactly the ungoverned AI use this exists to catch.

So every AI-suggested answer stays flagged until a human confirms it. The final report is only issued when nothing is left unverified. It's the framework practising what it preaches.

AI
Reads your policy, setup notes, or call summary and drafts a score across all controls.
AI · VERIFY
Each suggestion is flagged — clearly marked as unverified in the tool.
HUMAN
The assessor confirms or overrides every answer, backed by evidence.
SIGNED OFF
Report issued only when zero items remain unverified.
Why now

You don't need a new law for this to matter

There's no UK AI Act coming soon — and that's exactly why businesses are confused. AI is governed through several overlapping regimes, and the pressure is already arriving.

Feb 2026 · in force

Data (Use and Access) Act

New UK GDPR Articles 22A–22D require documented safeguards — transparency, human review, right to contest — around automated decisions.

ICO

Statutory AI code

The ICO is producing a code of practice on AI and automated decision-making, with active work on AI in the workplace.

Customers & insurers

Due-diligence questions

Enterprise procurement and insurance renewals increasingly ask suppliers how they govern AI. Most have nothing to show.

The common thread across every one of these is documentation: a named owner, a register of what's in use, evidence of oversight. That's precisely what Validara produces.

What Validara is — and isn't

  • It's an evidence-based snapshot, not a legal certification. A high score means you can evidence good controls — not that you're guaranteed safe or compliant.
  • It's point-in-time. AI estates change weekly, so the living register matters as much as the one-off score.
  • It's led by a quality-engineering background, not a compliance box-ticking one. 15+ years testing software for a living means the instinct is evidence and reproducibility — building toward alignment with recognised standards (ISO/IEC 42001, NIST AI RMF), while being upfront that it's an independent practice, not an accreditation body.
  • It's continuous, not a one-off. Consultants run an audit and leave; enterprise software is built for companies with security teams. Validara keeps a small business's register honest quarter after quarter — including the boring bits: what you're spending, and what's still switched on that shouldn't be.
  • It doesn't test the AI models. It measures how well your use of them is governed. Different question, and the one nobody's answering.
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Find out what's really running in your business

A structured AI assessment: full inventory, a risk score for every tool, and a report you can hand to any customer, insurer, or auditor who asks.