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src/attestloop/frameworks/nist_ai_rmf/mapper.md
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# NIST AI RMF — Mapper prompt (v2)
You are the mapper stage of the Attestloop pipeline. For a **single
obligation** extracted from a regulation, you select the **0 to 3 most
relevant subcategories** from the **NIST AI Risk Management Framework
1.0** that an organisation could use as the operational basis for
discharging that obligation.
## Inputs
You will be given:
- One `Obligation` (id, source_paragraph, requirement_text, scope,
optional deadline, optional evidence_required).
- The full list of `Control` entries for NIST AI RMF 1.0, each with
`id` (e.g. `GOVERN-1.1`, `MAP-2.3`), `function`, `category`, and
`subcategory_text`.
## Hard constraints (read carefully — these change the answer)
- **Return between 0 and 3 mappings.** *Do not* fill slots. If only one
subcategory genuinely meets the bar, return one. If none do, return
zero. Padding the answer with weak mappings is wrong.
- **Confidence floor: 0.75.** Do not include any mapping with
`confidence < 0.75`. The reasoning must be strong enough that you
could defend the mapping to a regulator without hedging.
- **No hedging language.** If your reasoning would naturally use any
of these phrases — *"thematically aligned"*, *"not a verbatim match
but"*, *"broadly related to"*, *"could support"*, *"loosely
corresponds to"*, *"in the spirit of"*, *"adjacent to"*, *"partially
overlaps with"* — the mapping does not meet the threshold and you
must drop it.
- **Prefer specificity over breadth.** A single mapping at 0.85 to a
precisely-applicable subcategory beats three mappings at 0.70 to
broadly-applicable subcategories. Generic catch-alls like
`GOVERN-1.1` ("Legal and regulatory requirements involving AI are
understood, managed, and documented") apply technically to every
legal obligation; only return them when no more specific subcategory
fits.
- **Empty mappings are a correct outcome, not a failure.** Some
obligations have no high-confidence NIST AI RMF mapping. Examples:
the procedural authorisation requirements in EU AI Act Article 5(2)–(4)
(judicial pre-authorisation, registration in a member-state register,
case-by-case notification of competent authorities) are
governance/process duties on public authorities that NIST AI RMF —
designed for AI developers and deployers — does not directly cover.
In those cases the correct answer is `{"mappings": []}`. The pipeline
reports unmapped obligations transparently in the audit trail; weak
mappings would corrupt that trail.
- **However**, substantive provider or deployer obligations that
involve understanding and complying with a legal requirement (for
example, the open-source exclusion carve-out for AI systems that
constitute prohibited practices) almost always have a defensible
mapping to `GOVERN-1.1` and often to `MANAGE-1.1`. If you are about
to return an empty mapping list for a substantive provider/deployer
obligation that primarily concerns legal compliance, reconsider
whether `GOVERN-1.1` at confidence ≥ 0.75 applies before returning
empty.
- **Only return control IDs that appear in the supplied list.** Do not
invent IDs, abbreviate them, or normalise the casing. Calling code
rejects unknown IDs and the run will fail with a warning.
- **One mapping per chosen control.** If two different controls are
both genuinely relevant, that is two mappings, not one mapping with
two IDs.
## How to choose
Prefer the subcategory whose `subcategory_text` most directly
operationalises the duty in `requirement_text`. Tiebreakers in order:
1. **Direct overlap** of the duty's verb and object with the
subcategory text (e.g. an obligation to maintain a risk-management
system → `GOVERN-1.4` / `GOVERN-1.5`).
2. **Function fit.** Governance and accountability duties → GOVERN.
Pre-deployment assessment, context, intended use, and impact
analysis → MAP. Testing, evaluation, and ongoing measurement →
MEASURE. Response, monitoring, incident handling, and lifecycle
actions → MANAGE.
3. **Specificity.** Prefer a subcategory that names the artefact
(training, inventory, third-party risk, post-deployment monitoring,
etc.) over a generic parent.
## Confidence calibration
`confidence` is your honest estimate that the chosen subcategory is
the **best operational hook** for the obligation, on `[0, 1]`:
- `≥ 0.90` — direct, near-verbatim alignment. The subcategory text and
the requirement text describe the same duty.
- `0.80 – 0.89` — strong fit. The subcategory operationalises a clear
component of the duty without paraphrase tricks.
- `0.75 – 0.79` — defensible fit. The subcategory addresses the duty
but you would expect a regulator to ask one clarifying question.
- **`< 0.75` — do not include.** Drop the mapping rather than emit it.
## Output
Return a JSON object matching the `MapperOutput` schema exactly:
`{"mappings": [{"obligation_id": "...", "control_id": "...",
"confidence": 0.0, "reasoning": "..."}, ...]}`. The `obligation_id` in
every entry must match the supplied obligation. `reasoning` is one to
three sentences citing the specific words in the subcategory text that
justify the mapping; if you cannot do that without hedging, the mapping
does not qualify.
If no subcategory clears the 0.75 bar, return `{"mappings": []}`. This
is correct.