Introducing Alfred Evidence, owners, and decisions ready for the next request. See what Alfred connects

Know whether AI governance operates beyond policy

Examine decision rights, lifecycle controls, human oversight, and evidence across material AI use cases.

The business pressureName what must change.
01

Use cases spread faster than inventory

Models, features, vendor services, and employee tools appear without a complete record of purpose, data, impact, or sponsor.

02

Evaluation is hard to interpret

Testing exists, but criteria, data, limits, approval, and deployment relevance are not evident.

03

Responsibility fragments

Product, engineering, data, legal, privacy, security, procurement, and business each control part of the lifecycle.

01 · What we deliver

What we examine and report.

We begin with the decision the intended user needs to make. The work then shows what the evidence supports, where gaps remain, and what requires action.

01 · Deliverable

AI assurance terms

Selected use cases, lifecycle stages, entities, criteria, evidence, period, exclusions, and report users.

02 · Deliverable

Use case evidence profile

Purpose, affected users, sponsor, model and data dependencies, deployment, human involvement, and known limits.

03 · Deliverable

Governance examination

Independent review of intake, classification, authority, approvals, exceptions, escalation, reporting, and oversight.

04 · Deliverable

Lifecycle evidence examination

Review of selected sourcing, data, design, evaluation, release, monitoring, change, incident, and retirement records.

05 · Deliverable

AI assurance findings

Observed conditions, evidence, affected objective, dependency, significance, and limitation.

06 · Deliverable

Independent AI assurance report

Scope, method, use case observations, findings, limitations, and management decisions required.

Evidence you can use

Evidence your team can use after delivery.

Each output identifies its source, owner, review point, and next action so the work stays traceable after handoff.

What you receive
  • AI assurance terms
  • Use case evidence profile
  • Governance examination
  • Lifecycle evidence examination
  • AI assurance findings
  • Independent AI assurance report
How it stays useful
Source
Current source material
Owner
Named owner
Timing
Relevant period
Status
Review status and decision
02 · The Open method

From a precise question to a usable conclusion.

The question, evidence, testing, and conclusion remain easy to follow. Leaders can see what was examined, what was found, and how the result should be used.

01 · Question

Define

Agree the decision, audience, subject, expectations, timing, dependencies, and type of review before testing begins.

02 · Evidence

Examine

Review the relevant records, configurations, conversations, and technical evidence. We test whether it is current, reliable, and sufficient for the question.

03 · Judgement

Challenge

Follow exceptions, conflicting evidence, and gaps. The conclusion follows what the work shows, not the preferred story.

04 · Decision support

Communicate

Explain findings, implications, uncertainty, and the next decision in language the audience can use.

03 · Clear roles

Keep authority clear at every handoff.

Open leads the agreed work. Your team keeps management decisions. Independent reviewers and qualified specialists retain the authority only they can hold.

01 · How we frame it

One clear assurance question

Before we begin, we confirm the question, evidence, review approach, audience, and reporting format. Any change remains visible.

02 · What your team owns

Management keeps ownership

Your team remains responsible for systems, controls, records, remediation, and the information it provides. We examine; we do not take over management decisions.

03 · When a specialist is required

Use the right qualified provider

If law or a professional standard requires a licensed or accredited report, we make the qualified delivery path clear from the start.

Business results

What changes after the work.

The result should change what the team can do next: reduce exposure, operate a stronger control, answer scrutiny, or make a decision with better evidence.

01 · Outcome

Oversight connects to use cases

Leaders see how governance applies to selected models, products, vendors, data, and affected users.

02 · Outcome

Evaluation retains context

Results include criteria, data, deployment relevance, limitations, and approval records.

03 · Outcome

Lifecycle gaps become specific

Teams can locate missing records across sourcing, release, monitoring, change, and retirement.

Common questions

What to settle before work starts.

Direct answers on fit, timing, responsibilities, deliverables, and the next commercial step.

Which AI uses should be examined first?

Prioritize uses affecting people, important decisions, sensitive data, critical operations, customers, or fast changing suppliers.

What evidence is relevant?

Use case inventories, impact assessments, data records, evaluation plans and results, approvals, vendor documents, monitoring, incidents, and change records.

How do you examine third party AI?

We connect vendor evidence and terms to actual use, data, configuration, monitoring, and fallback arrangements.

Does the work evaluate model performance?

It can examine how evaluations are governed. Independent technical testing needs explicit criteria, data, access, methods, and scope.

Is this certification or a safety guarantee?

No. It does not certify a model or organization, guarantee performance, or replace legal and regulatory interpretation.

Test how AI governance works in practice

Bring the use cases, governance claims, and stakeholders that need an independent view.