Define test and evaluation, human oversight, traceability, release gates, failure handling, and model-risk controls.
Illustrative service visualFIG 01 · GOVERNANCE EVIDENCE — Context and risk classification, policy controls, evaluation, human review, release gates, and incident evidence structured into one operating loop.
Risk-tiered
Review
Mapped evidence
Controls
Control map
Output
Concept interface · illustrative valuesGovernance evidence
Safety and governance work now has a visual operating model for risk classification, model review, incident response, and control evidence.
AI Assurance & TEV&V reference path
Scope modelContext, obligations, and claims
Layer 01
Evaluation and assurance environment
Layer 02
Release and authority gate
Layer 03
Operational governance loop
Layer 04
Acceptance
Requirement-to-test traceability
Acceptance
Repeatable evaluation evidence
Acceptance
Effective human authority
Acceptance
Lifecycle response rehearsal
Policies alone do not show whether an AI-enabled system is suitable for its intended use. Assurance and TEV&V connect requirements, data, models, operators, failure modes, evaluation results, approvals, monitoring, and incidents into a reviewable lifecycle.
P01
Broad principles such as reliability, explainability, fairness, or human oversight remain ambiguous until translated into system-specific claims and tests.
Decision question
Which requirement applies to which use, component, user, and failure consequence, and what evidence is sufficient for the next gate?
P02
A nominal human-in-the-loop control is weak when the operator lacks time, context, authority, or a usable way to challenge the system.
Decision question
Who is accountable, what can that person observe and override, and under which conditions must the system stop or revert?
P03
Aggregate benchmark scores can hide subgroup failures, distribution shift, adversarial behaviour, tool misuse, degraded environments, and operator misunderstanding.
Decision question
Which representative, edge, adversarial, and failure cases reflect the actual operating context and its consequences?
P04
Models, prompts, data, policies, dependencies, environments, and user behaviour change after the initial review and may invalidate earlier evidence.
Decision question
Which changes trigger reevaluation, reapproval, rollback, incident response, or retirement?
Governance begins with intended use, affected people, operating conditions, prohibited uses, and the consequence of failure. Terms such as reliable, explainable, secure, or human-supervised are rewritten as system-specific claims with owners, evidence methods, and limits, allowing reviewers to distinguish an obligation from a general aspiration.
The evaluation plan follows those claims into representative tasks, subgroup and edge cases, adversarial behavior, degraded conditions, and human-factors review. Test coverage is chosen because it informs a release question, not because a benchmark is available, and every result stays tied to the exact data, model, prompt, policy, dependency, and environment evaluated.
AI Assurance, TEV&V & Governance is delivered as inspectable engineering work. Each package states what enters the process, what leaves it, and what the evidence does not prove.
Define intended use, prohibited use, affected parties, operating environment, consequences, accountability, and applicable internal or external requirements.
Translate system claims and failure consequences into datasets, scenarios, metrics, qualitative reviews, operator studies, and repeatable evaluation records.
Exercise misuse, prompt injection, data poisoning indicators, evasion, overreliance, automation bias, uncertain output, component failure, and degraded operation.
Define the decision forum, evidence pack, release conditions, monitoring signals, change triggers, incident roles, rollback, review cadence, and retirement path.
This is a scoping architecture, not a claim that every product or environment uses the same stack. Interfaces and owners are confirmed against the actual deployment.
01
Layer 01
Tie intended use, users, consequences, prohibited uses, requirements, accountable owners, and system claims to a controlled record.
Typical elements
AI system inventory, context profile, risk tier, control crosswalk, claim register, RACI, and data-use decision.
02
Layer 02
Run versioned task, subgroup, robustness, security, human-factors, and failure evaluations with reproducible inputs and review notes.
Typical elements
Golden dataset, scenario library, simulator, adversarial cases, evaluator rubric, trace store, and coverage report.
03
Layer 03
Combine technical evidence, unresolved issues, operator controls, security review, residual risk, and decision authority before release.
Typical elements
Evidence pack, exception register, approval workflow, signed release manifest, operating limit, and stop condition.
04
Layer 04
Connect monitoring, user feedback, incidents, changes, drift, complaints, corrective action, reevaluation, and retirement to named owners.
Typical elements
Telemetry and feedback signals, incident record, change trigger, periodic review, corrective action, rollback, and retirement decision.
The assurance architecture joins context and requirements to evaluation records, residual issues, operator controls, and a named release authority. Exceptions and operating limits remain visible at the gate, so a favourable aggregate score cannot obscure an unresolved failure path or transfer risk without an explicit decision.
Handover establishes which telemetry, feedback, incident, supplier, data, or configuration changes trigger review. It also rehearses containment, rollback, corrective action, and retirement responsibilities. The client retains approval and residual-risk authority while the evidence record gives future reviewers a traceable starting point for reevaluation.
These profiles show how the service changes by operating context. They are examples for scoping—not customer case studies or pre-approved outcomes.
U01
An AI-enabled decision-support or supervised-autonomy component will operate with real users, degraded conditions, and explicit human responsibility.
U02
An internal assistant retrieves sensitive knowledge, drafts content, and may prepare tool actions for employee review.
U03
A model, runtime, sensor, or policy update may change behaviour across a device fleet operating under resource and connectivity constraints.
A detailed page should make the boundary as understandable as the capability. Final commitments still live in the signed statement of work.
Included in this service pattern
Not implied by this page
A01
Each in-scope claim and control links to an owner, test or review method, result, limitation, issue, and release decision for the named system version.
A02
The approved evaluation set, environment, model, prompt, policy, dependency, and scoring versions can be rerun and compared without reconstructing missing context.
A03
Representative scenarios show that authorised users can recognise system state and uncertainty, challenge or override output, escalate, and invoke the declared stop or fallback path.
A04
A simulated issue or change follows the agreed detection, triage, evidence capture, authority, containment, rollback, communication, corrective action, and reevaluation path.
Discovery questions
References shape requirements and review questions. Inclusion does not imply certification, endorsement, partnership, or approval by the publisher.
Each artifact has an owner, source context, review state, and a defined role in the next decision or release gate.
Engagement artifacts
04 records per engagement
Build reviewable AI assurance and governance evidence around the requirements that apply to the actual use case.
