Source: Rivial Security Blog
Author: Lucas Hathaway
URL: https://www.rivialsecurity.com/blog/ai-inventory-template
https://www.rivialsecurity.com/blog/ai-inventory-template
ONE SENTENCE SUMMARY:
Financial institutions need a living AI inventory to track AI usage, ownership, data, risks, controls, and evidence for governance.
MAIN POINTS:
- AI inventories provide a governed system of record, not a static spreadsheet.
- NIST AI RMF Govern 1.6 calls for inventory mechanisms aligned to risk priorities.
- Scope must include internal models, embedded vendor AI, and employee-used generative tools.
- Undocumented AI creates gaps in data handling, accountability, explainability, and control ownership.
- Interagency third-party risk guidance requires lifecycle oversight even when AI is outsourced.
- Executive reporting improves by slicing inventory data by unit, tier, vendors, and control maturity.
- Core fields include owners, purpose, vendor/build type, data sensitivity, and outputs influenced.
- Risk-tiering enables proportionate reviews based on impact, sensitivity, oversight, and regulatory exposure.
- Inventory value increases when linked to approvals, workflows, control mapping, and evidence locations.
- Common failures include missing vendor AI, lacking ownership, ignoring data context, and omitting control linkage.
TAKEAWAYS:
- Build inventories to support governance decisions, not to “complete a checkbox.”
- Capture third-party and embedded AI to avoid false completeness about institutional exposure.
- Assign both business and technical/security ownership to ensure updates and remediation happen.
- Record input data types and sensitivity to drive privacy, security, and compliance requirements.
- Keep review dates/status and evidence pointers so audits, exams, and boards get defensible answers.