The DDQ arrives from an allocator with AI questions in it, an exam request arrives from the SEC, and a question arrives from the board, each asking for the same evidence: what AI is in use, who approved it, on what criteria, and how it is monitored. Assembled by hand, the answer takes weeks and is stale on delivery.
An allocator comparing twenty managers’ responses notices which answers came from a system. Those answers quote current numbers, name reviewers, and cite dates. The others read alike. Clarier produces a DDQ evidence pack drawn from live platform data, mapped to the AI questions in AIMA’s 2025 questionnaire theme by theme, submitted alongside the DDQ and updated in place as the estate changes.
Note: AIMA’s DDQ is member-proprietary. Themes are described in our own words; we do not reproduce AIMA’s question text.
SEC exam priorities name AI use and disclosure, and a request list gives you days, not quarters. The same evidence pack answers the examiner: inventory, approval records, vendor diligence, and the audit trail behind each decision, mapped to what examiners actually ask for.
Quarterly board questions draw from the same live data: a maturity score across the program’s published pillars, current inventory and spend, and open items with owners. The report is exported, not written, so it is never stale on arrival.
Trigger: An LP DDQ arrived with a new AI section covering inventory, policy, vendor diligence, and monitoring.
Deployment: Baseline in the first weeks across the existing identity and endpoint stack; approval workflows loaded with the firm’s criteria.
Result: 2 days to return the full AI section, evidence attached.
Illustrative composite.
Governance built as enablement: yes is the fast answer, with the receipts attached.
A short call, on your environment: where you stand today, and what a regulator would see if they asked.