AI Governance Compliance Reconciliation
An evidence-led process for reconciling AI-tool access with required training, preserving uncertainty, and turning fragmented records into an auditable follow-up workflow.
Confidential Context
This case study is sanitized. Client data and proprietary integrations are omitted. Internal Lean Tech governance engagement. User rosters, employee identities, platform exports, forms, and company communications are omitted.
Outcomes
- Separated platform access, course completion, tool approval, and communication status instead of conflating them
- Reconciled multi-platform rosters with completion records while preserving duplicates, missing matches, and historical snapshots
- Produced a process map, review findings, recipient candidates, evidence questions, and a formal communication draft
- Documented data-quality caveats so a provisional tracker could not be presented as unquestionable compliance evidence
Problem
AI governance programs often start with disconnected exports: users from one platform, certificates from another, a course page, a form, and a deadline mentioned in a meeting. Treating any one file as the whole truth creates false exclusions and false compliance.
The task was to turn those fragments into a traceable process for identifying the right population, matching completion evidence, communicating with pending users, and keeping unresolved questions visible.
Approach
- Defined the target population as the union of approved AI-tool rosters rather than a single platform export.
- Kept course completion, platform access, business need, and tool approval as separate fields with separate owners and evidence.
- Profiled and deduplicated source files, preserved unmatched rows, and treated alternate email addresses and missing names as review states rather than silently dropping them.
- Produced a process map and reconciliation rules covering snapshots, inactive users, exceptions, official completion evidence, communication, and follow-up.
- Drafted a formal user communication with an explicit deadline, enrollment path, alternate-email correction route, and approved support channel.
What I delivered
I reviewed the source documentation, platform and certificate inventories, forms, and meeting context; identified evidence gaps; proposed the reconciled workflow; and prepared the review outputs needed for the governance team to make the remaining policy decisions.
Interview summary
I designed an evidence-led AI-governance reconciliation process that keeps access, training, approval, and communication distinct, preserves uncertainty, and gives an operations team a defensible follow-up queue.