Solution Assurance · AI Assurance Verification
Independent verification for AI systems, agents and AI-enabled services.
AI Assurance Verification helps organisations test whether the assurance supporting an AI system is current, scoped to the system actually in use, and supported by available evidence and configuration records.
When to use it
Answer the questions that come before reliance.
Use AI Assurance Verification when you need to explain, evidence or challenge the assurance position for an AI system before relying on it, granting it access, presenting it to customers, reporting to the board or using it in a higher-risk workflow. In particular, before you can answer:
Questions this answers
- What assurance evidence supports this AI system?
- Does the evidence match the system actually in use?
- Is the evidence current, expired, incomplete or unclear?
- Which responsibilities sit with the model provider, platform provider, application builder, deployer or other parties?
- What assurance depends on artefacts, configuration records, contracts, logs, evaluations or human judgement?
- Are there areas that remain unverifiable?
- If agents are involved, what systems, tools or credentials can they access?
Typical use cases
Where this applies.
Any AI system your organisation relies on, or is being asked to rely on, whether it was built, bought or embedded. Verification is commissioned before reliance, during procurement or vendor review, for board and risk committee reporting, around ISO/IEC 42001 readiness and evidence strengthening, and to support customer due diligence responses.
Suitable for
- AI agents with access to business systems
- AI-enabled SaaS products
- Managed AI platforms, such as cloud-hosted model services
- Customer-facing AI features
- Internal AI tools used in material workflows
Scope of review
What we review.
The scope is agreed for each engagement, and cyber security and responsible AI are covered where they are material to it. Depending on the system, the review draws on the evidence sources listed here. Where an expected source does not exist, that absence is itself a finding. Evidence is examined under agreed handling and retained only for the engagement period.
Possible evidence sources
- AI system description and intended use
- Responsibility allocation across relevant parties
- AI policy, risk and impact assessment records
- Provider assurance evidence, such as certificates, reports, model and system cards, evaluation summaries, and model version or change notices
- Data handling, retention and training-on-customer-data settings
- Prompt, orchestration, RAG and guardrail design records
- Evaluation, regression testing or red-team evidence
- Logging, monitoring and incident records
- Agent tool inventory, access grants, approval thresholds and runtime bounds
- Cloud, identity or platform configuration exports where relevant
Verification states
Not just whether evidence exists.
The report classifies each assurance area using practical verification states, rather than a single pass/fail conclusion.
Current
Evidence exists, is current and appears scoped to the system under review.
Expiring
Evidence exists but is approaching its currency limit.
Exception
Evidence is missing, stale, inconsistent, incomplete or not aligned to the system under review.
Unverifiable
The evidence or access required to verify the claim was not available.
Judgement required
Evidence informs the conclusion, but professional judgement is required.
What you receive
A report built for reliance.
Every deliverable is written to be handed on: to a board, a customer, a procurement team or an external auditor. Findings state what was verified, what was not, and what that means for the reliance being placed on the system.
Typical deliverables
- AI Assurance Verification Report
- Evidence map
- Responsibility allocation summary
- Verification state summary
- Exceptions and limitations
- Agent access review, where applicable
- Practical recommendations
- Executive summary suitable for board, customer, procurement or assurance stakeholders
Clear boundaries
What this is not.
Clear boundaries protect the conclusion. The engagement verifies available evidence, configuration records and responsibility allocations within the agreed scope. It does not prove hidden model-provider controls, guarantee future AI behaviour or remove the need for accountable human judgement. It is a scoped professional service, available by fit check.
Not included
- ISO/IEC 42001 certification
- Legal advice
- A model safety certification
- A penetration test
- A financial audit
- An insurer rating
- A guarantee that an AI system is safe
- A replacement for management accountability
RELATIONSHIP WITH ISO/IEC 42001
ISO/IEC 42001 INTERNAL AUDIT
Tests whether an AI management system conforms to the standard and is operating as intended.
AI ASSURANCE VERIFICATION
Narrower and system-focused. Tests whether the assurance supporting a particular AI system, agent or AI-enabled workflow is current, scoped and supported by available evidence.
Some organisations use it before an ISO/IEC 42001 audit to identify evidence gaps. Others use it after implementing an AI management system, to strengthen assurance over specific AI deployments.
RELATED ASSURANCE LAYER
AI ASSURANCE VERIFICATION
Examines a specific deployed AI system, agent or use case.
VENDOR REVIEW
Need to assess the supplier rather than the deployed solution? Vendor Review examines the vendor and the service it provides, scoped to a real onboarding, renewal or reliance decision.
Not sure if this applies to your AI system?
The fit check takes about two minutes and asks for no documents.