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.
What you gain
Assurance your team can use.
Defensible findings
Conclusions linked to the criteria and evidence examined.
Clear priorities
Know what matters first and what can wait.
Less disruption
Evidence is reviewed before follow-up begins.
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.
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
- Procurement or vendor assurance reviews
- Board or risk committee reporting
- Pre-ISO/IEC 42001 readiness work
- Post-ISO/IEC 42001 evidence strengthening
- Customer due diligence responses
Scope of review
What we review.
The scope is agreed for each engagement. Depending on the system, the review may cover:
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 cards, system cards or evaluation summaries
- Model version and change notice evidence
- 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, not just review.
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.
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
The service 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.
AI Assurance Verification is a scoped professional service, available by fit check. It is not certification, legal advice, insurer underwriting, a model safety guarantee or a substitute for accountable governance.
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.
Not sure if this applies to your AI system?
The fit check takes about two minutes and asks for no documents.