05 / AI governance

Useful intelligence. Clear limits. Accountable decisions.

Enterprise AI must fit the permissions, evidence and professional responsibilities of the workflow it supports. A convincing answer is not necessarily a correct or authorised answer.

Review control framework ↗Evidence & responsibilities
Context and scope

What this means
in an enterprise review.

Numerish AI-enabled functions may support discovery, document drafting, triage, analysis and recommendations. The exact provider, configuration, data flows and human review requirements must be established for each enabled feature.

The descriptions below outline control expectations and evidence to evaluate. They do not claim that a specific Numerish product has completed independent certification.

Control objectives

The six areas
to assess.

Each domain brings a specific objective and a practical review item. Choose the level of assurance needed for the relevant service and regulatory context.

DOMAINCONTROL OBJECTIVEREVIEW EVIDENCE
01

Feature and use-case inventory

Document where AI is used, why it is needed, which decisions it influences and which uses are prohibited.

Request or inspect

Feature register, risk classification and approved intended-use statement.

02

Data and model boundaries

Specify permitted input categories, provider processing, retention, training-use terms and geographic transfers.

Request or inspect

AI data-flow assessment, provider terms and tenant permissions.

03

Human control and escalation

Require appropriately qualified review for financial, legal, engineering, health and other consequential outcomes.

Request or inspect

Approval matrices, override/appeal route and supervised workflow tests.

04

Accuracy, bias and reliability

Test model performance against realistic cases, including difficult inputs, hallucinations and policy violations.

Request or inspect

Independent factuality and fairness evaluations, red-team findings and remediation outcomes.

05

Transparency to users

Tell users where output is AI-assisted, what it does not verify, and why a recommendation should not be mistaken for a formal approval.

Request or inspect

Product user guidance, human-review prompts and explainability artefacts where supported.

06

Ongoing monitoring and provider changes

Reevaluate risks when models, prompts, underlying data, integrations or provider settings change.

Request or inspect

Change logs, evaluation cadence, drift reporting and incident procedures.

Responsibility & acceptance

Clear ownership.
Clear evidence.

The dividing line between supplier capability and customer responsibilities should be agreed before implementation or procurement sign-off.

Numerish / subject to product scope

What Numerish should demonstrate

  • 01Feature and use-case inventoryFeature register, risk classification and approved intended-use statement.
  • 02Data and model boundariesAI data-flow assessment, provider terms and tenant permissions.
  • 03Human control and escalationApproval matrices, override/appeal route and supervised workflow tests.
  • 04Accuracy, bias and reliabilityIndependent factuality and fairness evaluations, red-team findings and remediation outcomes.
  • 05Transparency to usersProduct user guidance, human-review prompts and explainability artefacts where supported.
  • 06Ongoing monitoring and provider changesChange logs, evaluation cadence, drift reporting and incident procedures.
Customer / governed administration

Your organisation remains responsible for

  1. Approve permitted AI use cases and provide suitable professional oversight.
  2. Avoid submitting data without authorisation or the necessary contract.
  3. Review consequential outputs before relying on them.
  4. Monitor actual outcomes and report quality or safety issues.
Scope boundary

No general claim that every Numerish AI feature is externally certified, medically validated, safe for autonomous professional approval or suitable for unrestricted sensitive-data input.

Assurance engagement

Validate the controls
for your environment.

Request product- and version-specific artefacts; identify the relevant jurisdiction, data classes, hosting model and procurement requirements.

Request relevant evidence ↗Documents may be subject to availability, authorisation and confidentiality requirements.
Frequently asked

Important clarifications.

Controls and independently verified outcomes should not be confused.

Can AI-generated designs or legal advice be used without professional review?

No. Outputs can be wrong; regulated and safety-critical uses demand competent human verification and the applicable statutory approvals.

Are customer inputs used to train a model?

The answer depends on the feature, provider and signed processing terms. Request the applicable provider and data-use schedule rather than assuming one policy across all AI capabilities.