# Building a system that runs the AI inventory, policy and reporting that the board and supervisors ask for inside a bank

Source: https://www.bankingnewsai.com/agentic-banking/build/governance-general
Last updated: Sep 17, 2026

Governance teams can use the tools to run governance: keep the inventory current from the systems that know what is deployed, map each use case to the documents that apply, draft the board report and the regulatory responses, and check policies against new rules as they land. It is a chained workflow over a stable corpus of rules and internal policy, with a person owning every document that leaves the team. The regulation tracker on this site is the kind of corpus it reads.

## The brief (default answers)

| Choice | Answer |
| --- | --- |
| Who is affected | Staff only |
| Reversibility | Fully |
| Stakes | Material |
| Knowledge | Stable documents |
| Verifiability | By judgment |
| Steps | Known |
| Jurisdiction | More than one |
| Delivery route | Provider API |

- **Pattern:** Workflow — Several model calls orchestrated by your code, in a sequence or a graph you designed.
- **Tier:** 3, Tier 3: act within an envelope — Low stakes, reversible, internal. The model may act within a defined envelope, with sampling, monitoring and a kill switch instead of per-action review.
- **Workflow shapes:** Prompt chaining, Evaluator and optimizer
- **Human involvement:** The model may act within a defined envelope; people sample, monitor and can stop it.
- **Knowledge:** Retrieval over the governed document set, with a citation on every answer and a refusal when nothing relevant is found.

## Decomposition

| Step | Owner | Note |
| --- | --- | --- |
| Maintain the inventory | system | From deployment records and the model repository; structured fields. |
| Map use cases to rules | model | Against the governed corpus of regulation and policy, with citations. |
| Draft board and regulator reporting | model | From the inventory, the evals and the exception logs. |
| Check policy against new rules | model | Gap analysis when a document changes status; proposals only. |
| Decide and sign | human | The chief risk officer or equivalent owns what is sent. |

## Control layer weights (0–4)

| Layer | Weight |
| --- | --- |
| governance | 1 |
| identity | 0.55 |
| actions | 0.4 |
| data | 1 |
| models | 0.55 |
| runtime | 0.8 |
| observability | 0.7 |
| oversight | 0.6 |

## Documents that apply

- [SR 26-2](https://www.bankingnewsai.com/ai-regulation/documents/fed-sr-26-2) — Board and senior-management governance of models; the generative and agentic carve-out.
- [NIST AI RMF 1.0](https://www.bankingnewsai.com/ai-regulation/documents/nist-ai-100-1) — Govern, Map, Measure, Manage: the voluntary frame most US banks cite.
- [Treasury FS AI RMF and AI Lexicon (Feb 2026)](https://www.bankingnewsai.com/ai-regulation/documents/treasury-fs-ai-rmf-and-ai-lexicon-2026) — The NIST framework adapted to financial services.
- [FSB AI sound practices consultation (June 2026)](https://www.bankingnewsai.com/ai-regulation/documents/fsb-ai-sound-practices-consultation-2026) — Twelve sound practices from strategy to third-party risk.
- [Machado speech: 'Technology is neutral, governance is not' (Feb 2026)](https://www.bankingnewsai.com/ai-regulation/documents/ecb-machado-technology-neutral-governance-speech-2026) — Accountability, senior oversight, effective challenge.
- [Regulation (EU) 2024/1689](https://www.bankingnewsai.com/ai-regulation/documents/eu-ai-act-regulation-2024-1689) — The EU AI Act sets the strictest documentation floor.
- [PRA SS1/23](https://www.bankingnewsai.com/ai-regulation/documents/pra-ss1-23-model-risk-management) — UK: the widest model perimeter.
- [SR 23-4](https://www.bankingnewsai.com/ai-regulation/documents/fed-sr-23-4) — The model provider is a third party: due diligence, contract terms, monitoring, exit.
- [FSB AI monitoring report (Oct 2025)](https://www.bankingnewsai.com/ai-regulation/documents/fsb-monitoring-ai-adoption-vulnerabilities-2025) — Concentration on a small number of model suppliers.

## Evals

- A golden dataset of at least 50 real cases with expected outputs, including adversarial inputs: wrong documents, unusual formats, prompts that try to change the task.
- A judge model scoring against a written rubric (accuracy, completeness, tone, citation present), calibrated against a human-scored sample every month. Threshold set from the human sample, not guessed.
- Citation checks: every factual claim resolves to a passage in the governed set; unsupported claims below 2% of answers.
- Trace evals per step, not only end to end: which step fails, how often, at what cost, so a prompt or model change can be judged step by step.
- The same suite reruns on every prompt change, model version change and retrieval change; a regression blocks the release. That is what ongoing monitoring and outcomes analysis mean in model-risk terms.

## Human gates

- An action envelope: which tools, which systems, what value, what volume, what the model may never do.
- Sampled human review of outputs and a weekly look at the exception log.
- Turn and cost budgets, logged, with an automatic stop when exceeded.
- A kill switch that any owner can pull.

## What an examiner will ask

- Where is this system in your inventory, what tier did you assign, and who signed it off?
- What counts as a model here, and what does your validation cover for the parts that are not?
- Show me the data lineage behind the retrieval set and the training or tuning data.
- What can the system do without a person, and where is that written down?
- How do you know it is still working: which evals run, how often, and what happened the last time one failed?
- What did you do about the vendor: due diligence, contract, exit plan, concentration?
- Walk me through one wrong output from production and what the customer, if any, saw.
- Who can switch it off, and has that been tested?

## What the board should hear

- What it does: runs the AI inventory, policy and reporting that the board and supervisors ask for; the model may act within a defined envelope; people sample, monitor and can stop it.
- Pattern: workflow (prompt chaining, evaluator and optimizer); tier 3: act within an envelope.
- Rules it answers to: 4 documents across several jurisdictions, each linked in the brief.
- How we know it works: a golden dataset, automated checks on every release, and human review at the level the tier demands.
- What could go wrong and who answers: the accountable owner, the kill switch, the escalation route.

## Pitfalls

- An inventory the model believes rather than one the systems report.
- Board papers that assert control effectiveness without the evals to show it.
- Rules mapped from memory instead of from a dated corpus.

Change any answer on the interactive map: https://www.bankingnewsai.com/agentic-banking/build/governance-general

---

Canonical page: https://www.bankingnewsai.com/agentic-banking/build/governance-general
Part of [BankingNewsAI](https://www.bankingnewsai.com/) — a free daily brief on AI in banking, an AI regulation tracker (19 authorities, 166 documents) and AI-strategy profiles of the 100 largest US banks. Markdown versions of every reference page: append `.md` to the page URL; index at https://www.bankingnewsai.com/llms.txt.