A dispute agent answers confidently.
It may miss the handoff, open the wrong case or access more account data than the workflow requires.
Wrong answer · missed escalation · accessAI assurance for regulated teams · start with one consequential use case
Can you show that your AI works as intended?
We start with one consequential AI use case and test it deeply: who owns it, what data and decisions it touches, what evidence exists and how it behaves under agreed scenarios. Your team gets documented gaps and next actions.
Start with a short scope conversation. No confidential material needed.
One use case
↗
Customer interactionWhat happens when the workflow meets a hard case?
01⌁
Internal decision supportWho can act on the output, and with what review?
02□
Control recordWhat was tested, by whom, and what changed?
03The workflow matters
AI failure takes different forms in different workflows.
Risks differ by use case, but each organization needs to know the system’s purpose, data access, owner, failure paths and evidence of testing.
A screening tool filters quietly.
It may shape employment decisions without a documented review of outcomes, exceptions and accommodation paths.
Outcomes · review · accommodationA helpful tool sends someone astray.
An unsupported summary can enter a clinical workflow. A public-service tool can send a resident to the wrong process.
Context · routing · escalation→
One system. One agreed boundary. Deep work inside it. We define what it is supposed to do, identify plausible failure paths, then use manual scenarios, agent-assisted test generation and replay, access checks, evidence review and repeatable records to examine that particular use case.
Illustrative failure paths, not claims about a client. Sources: CFPB · Chatbots in consumer finance · NYDFS · AI in insurance underwriting and pricing · NIST · AI RMF Generative AI Profile
Illustrative procurement scenario
A bank buys an AI dispute agent. What has actually been checked?
A third-party agent reads card transaction history, answers customer questions and creates dispute cases. The vendor provides a strong demo. The bank still needs to examine how the agent behaves with its data, permissions and handoff process.
No actual bank or incident is represented.
Questions the buyer should be able to answer
01Which account data can the agent read, and what can it write or transmit?
Start with the actual permissions, not the demo. Map what is readable, writable and transmissible in this workflow.
02Will it recognize a dispute and open the right case, or answer without starting the process?
A useful test follows the workflow through an expected handoff and a plausible failure, not just a successful happy path.
03Can untrusted text redirect the agent or expose information outside the user’s authorization?
Prompt injection and retrieval risks depend on the architecture, authorization model and what the agent can do.
04Who approves changes to prompts, tools and permissions—and who can stop it?
Name the accountable owner and the change path before launch.
Missing documentation does not itself create a breach. It can make some risks harder to identify, test, control and investigate.
A preparation tool
Five questions to ask about one AI system.
Pick a single use case. If you cannot find an answer, note the owner or record you would need.
This is not an assessment result. It does not collect system inputs or make a compliance determination.
01
What is this AI use case meant to do, and what must it never do?
02
Which data can it access, change or transmit, and who has approved that access?
03
Who owns the use case, vendor relationship and changes?
04
Which realistic failure cases were tested in your workflow, and where are the results?
05
How are errors, unexpected actions and affected people’s complaints detected and escalated?
If the answers are fragmented, we can scope a review of one system and its evidence
The review
How we examine your use case.
Ownership. Evidence. Testing. One use case is a boundary, not a shallow review. The depth is tailored to the system’s intended use, affected people and business process.
Ownership
Identify the system owner, intended use, approvals and human decision points.
Evidence
Examine the available records for controls, vendor inputs, changes and monitoring.
Testing
Manual review remains central. We inspect the workflow through human-led scenarios, evidence review and authorized tests. Agents may help generate and replay cases, but every result is reviewed and validated by a human assessor.
A scoped paid assessment
Useful documentation for the decision in front of you.
Start with one consequential use case. Test it deeply, document the evidence and expand the scope when the findings justify it. Final testing depth, timeline, price and legal mapping are agreed in the proposal.
Scope your reviewWhat your team gets
A defined system boundary and the failure cases in scope
An evidence record and documented test observations, including limitations
Prioritized findings with owners and practical next actions
View an illustrative sample finding Beyond one system
Broader regulatory mapping, when you need it.
We can separately map the requirements and recognized frameworks relevant to the jurisdictions and sectors where you operate, identify evidence and control gaps, and give you a prioritized remediation plan.
Discuss broader mappingExamples we may map — not an exhaustive list
ISO/IEC 27001Information security management systemsEU AI ActRegulation (EU) 2024/1689NIST AI RMFVoluntary AI risk management frameworkCalifornia SB 53Transparency in Frontier AI ActColorado SB24-205Consumer protections for AINYC Local Law 144Automated employment decision tools
Scope depends on your systems, use cases, locations and applicable obligations. This work supports compliance efforts; it is not legal advice, certification or a guarantee of compliance.
Relevant to New York financial services
Local context, carefully scoped.
New York financial institutions face different requirements depending on entity and use case. NYDFS has published guidance relevant to specified insurance AI uses, AI-related cyber risk and third-party risk. Banking model risk guidance may also be relevant.
We confirm the scope of any framework mapping for the particular engagement. New York City meetings may be arranged when founder availability is confirmed; remote assessments are available.
Questions teams ask
Clear scope before paid work begins.
We use a vendor’s AI model. Do we still need to assess our use case?
Vendor materials are one source of evidence. They may not cover your prompts, data access, human escalation, changes or tests of your own use case. We define the review around the deployed workflow.
What happens after I request a review?
We discuss the use case, relevant decision, stakeholders, scope, evidence availability and proposed timing. A paid assessment begins only after a separate scope and commercial agreement.
Does this apply outside banking and insurance?
Yes. We can scope one-system reviews for government, healthcare, HR and other regulated environments. The risks, evidence and applicable obligations depend on the actual workflow and jurisdiction.
Can missing use-case documentation cause a data breach?
Missing documentation alone does not cause a breach. A disclosure risk depends on the system design, access controls and behavior; we examine these within an agreed scope.
Start with a scope conversation
Tell us about the system and the decision ahead.
Share only a high-level description. We do not need confidential data, credentials, customer records or production prompts to decide whether a scoped review is a sensible next step.
Every submission routes to thyne.olivier@statuteos.com
A sensible first step
Choose one system to examine.
Tell us what the system is intended to do and what decision is coming up. We can agree on a sensible scope before paid work begins.