FedRAMP Moderate model release gate and golden set evaluation for enterprise AI — Reference Architecture

Reference architecture · published 2026-10-16 · regulated enterprise · FedRAMP Moderate · layout spec_first · implementation executed in CI before publish

Enterprise implementation parameters

Every value below is a parameter you can hold a vendor or an internal team to. Adjectives are not acceptance criteria.

The control that resolves this is a Frozen Golden Set Release Gate, evaluated before the request reaches a model provider rather than audited afterwards. In regulated enterprise the binding constraint is FedRAMP Moderate, and the question it actually asks is which NIST 800-53 control does each component satisfy. The shape below holds p95 ≤ 600ms over REST over HTTPS, and ships with a standard-library implementation that runs as written.

Where the rollout actually stalls

A model upgrade is approved on a demo and an aggregate score. Nobody can say which cases got worse, because the comparison is a single number against a test set that changes whenever it is inconvenient.

What FedRAMP Moderate specifically requires

FedRAMP is control-by-control. Every component must map to a specific NIST 800-53 Rev 5 control identifier with an implementation statement, and anything crossing the authorisation boundary needs its own justification in the SSP.

The artifact that satisfies it is a control implementation summary mapped to control identifiers. The way teams most often fail it: a component outside the authorisation boundary that nobody documented, found during assessment.

Working implementation — Per-slice release gate over a frozen golden set

Standard-library Python, no external dependencies, runs as written. Executed by the publish gate: if it raises, this page does not ship. Wire it behind REST over HTTPS; the failure mode it guards against is a retried POST creating a duplicate record. The output you hand the assessor is the SSP control implementation summary.

"""Release gate on a frozen golden set, with per-slice thresholds.

The failure this prevents: a candidate model improves the aggregate score and
regresses badly on one slice that happens to matter. Aggregates hide that
(Simpson's paradox is not a curiosity here, it is the default outcome), so the
gate evaluates per slice and refuses on any slice regression beyond tolerance
even when the headline number improves.
"""
from dataclasses import dataclass, field


@dataclass(frozen=True)
class Case:
    case_id: str
    slice_name: str
    prompt: str
    expected: str


@dataclass
class GoldenSet:
    """Frozen on purpose. A golden set edited to make a release pass is not a
    golden set, so mutation after freeze raises rather than warns."""
    name: str
    cases: list = field(default_factory=list)
    _frozen: bool = False

    def add(self, case: Case) -> None:
        if self._frozen:
            raise RuntimeError(f"{self.name} is frozen; create a new version")
        self.cases.append(case)

    def freeze(self) -> "GoldenSet":
        self._frozen = True
        return self

    def slices(self) -> set:
        return {c.slice_name for c in self.cases}


def score(model, golden: GoldenSet) -> dict:
    per_slice: dict[str, list] = {}
    for case in golden.cases:
        ok = model(case.prompt) == case.expected
        per_slice.setdefault(case.slice_name, []).append(ok)
    return {s: sum(v) / len(v) for s, v in per_slice.items()}


def aggregate(scores: dict, golden: GoldenSet) -> float:
    weights = {s: sum(1 for c in golden.cases if c.slice_name == s)
               for s in scores}
    total = sum(weights.values())
    return sum(scores[s] * weights[s] for s in scores) / total


class ReleaseGate:
    def __init__(self, tolerance: float = 0.02, floors: dict | None = None):
        self.tolerance = tolerance
        self.floors = floors or {}

    def decide(self, baseline: dict, candidate: dict, golden: GoldenSet) -> dict:
        regressions, floor_breaks = [], []
        for s in sorted(set(baseline) | set(candidate)):
            b, c = baseline.get(s, 0.0), candidate.get(s, 0.0)
            if b - c > self.tolerance:
                regressions.append({"slice": s, "baseline": round(b, 3),
                                    "candidate": round(c, 3),
                                    "delta": round(c - b, 3)})
            floor = self.floors.get(s)
            if floor is not None and c < floor:
                floor_breaks.append({"slice": s, "candidate": round(c, 3),
                                     "floor": floor})
        agg_b, agg_c = aggregate(baseline, golden), aggregate(candidate, golden)
        return {
            "aggregate_baseline": round(agg_b, 3),
            "aggregate_candidate": round(agg_c, 3),
            "aggregate_improved": agg_c > agg_b,
            "slice_regressions": regressions,
            "floor_breaches": floor_breaks,
            "release": not regressions and not floor_breaks,
        }


if __name__ == "__main__":
    g = GoldenSet("claims-triage-v1")
    for i in range(40):
        g.add(Case(f"c{i}", "common", f"p{i}", f"a{i}"))
    for i in range(10):
        g.add(Case(f"r{i}", "rare_high_value", f"rp{i}", f"ra{i}"))
    g.freeze()

    baseline_answers = {c.prompt: c.expected for c in g.cases}
    def baseline_model(p): return baseline_answers[p]

    # Candidate: better on the big slice, broken on the small critical one.
    def candidate_model(p):
        if p.startswith("rp"):
            return "wrong"
        return baseline_answers[p]

    base = score(baseline_model, g)
    cand = score(candidate_model, g)
    gate = ReleaseGate(tolerance=0.02, floors={"rare_high_value": 0.90})
    decision = gate.decide(base, cand, g)

    assert decision["aggregate_candidate"] >= 0.79, decision
    assert not decision["release"], "a slice regression must block the release"
    assert decision["slice_regressions"][0]["slice"] == "rare_high_value"
    assert decision["floor_breaches"], decision

    clean = gate.decide(base, base, g)
    assert clean["release"], clean

    try:
        g.add(Case("x", "common", "p", "a"))
        raise AssertionError("edited a frozen golden set")
    except RuntimeError:
        pass

    print("Evaluation release gate: OK")
    print(f"  aggregate {decision['aggregate_baseline']} -> "
          f"{decision['aggregate_candidate']}, release={decision['release']}, "
          f"slice regressions {len(decision['slice_regressions'])}")

The FedRAMP Moderate control, as code

NIST 800-53 control map with a boundary-crossing check. This is the artifact an assessor asks for under FedRAMP Moderate specifically — it is not the same control as the one above, and a system that implements one does not thereby implement the other. It executes under the same publish gate.

"""NIST 800-53 control satisfaction map with an authorisation-boundary check.

FedRAMP reviewers do not ask whether the system is secure. They ask which
control, by identifier, each component satisfies, and what crosses the
authorisation boundary. An external model endpoint is a boundary crossing, and
an undeclared crossing is the finding that stalls an ATO.
"""

MODERATE_BASELINE = {
    "AC-4": "Information flow enforcement",
    "AU-2": "Event logging",
    "AU-9": "Protection of audit information",
    "CA-3": "Information exchange / external connections",
    "SC-7": "Boundary protection",
    "SC-8": "Transmission confidentiality and integrity",
    "SC-28": "Protection of information at rest",
    "SI-4": "System monitoring",
}


class AuthorisationBoundary:
    def __init__(self, name: str):
        self.name = name
        self.components: dict[str, dict] = {}
        self.crossings: list[dict] = []

    def declare(self, component: str, inside: bool, controls: list[str]) -> None:
        unknown = [c for c in controls if c not in MODERATE_BASELINE]
        if unknown:
            raise KeyError(f"not in the Moderate baseline: {unknown}")
        self.components[component] = {"inside": inside, "controls": set(controls)}

    def connect(self, src: str, dst: str, declared: bool) -> None:
        for end in (src, dst):
            if end not in self.components:
                raise KeyError(f"undeclared component: {end}")
        crossing = self.components[src]["inside"] != self.components[dst]["inside"]
        self.crossings.append({"src": src, "dst": dst,
                               "boundary_crossing": crossing, "declared": declared})

    def undeclared_crossings(self) -> list[dict]:
        return [c for c in self.crossings if c["boundary_crossing"] and not c["declared"]]

    def coverage(self) -> dict:
        covered = set()
        for meta in self.components.values():
            covered |= meta["controls"]
        gaps = sorted(set(MODERATE_BASELINE) - covered)
        return {"covered": sorted(covered), "gaps": gaps,
                "ato_ready": not gaps and not self.undeclared_crossings()}


if __name__ == "__main__":
    b = AuthorisationBoundary("agency-llm-enclave")
    b.declare("gateway", True, ["AC-4", "SC-7", "SC-8"])
    b.declare("audit_store", True, ["AU-2", "AU-9", "SC-28"])
    b.declare("monitor", True, ["SI-4"])
    b.declare("external_model_api", False, ["CA-3"])

    b.connect("gateway", "audit_store", declared=True)
    b.connect("gateway", "external_model_api", declared=True)
    cov = b.coverage()
    assert cov["ato_ready"], cov
    assert cov["gaps"] == [], cov

    # The finding this exists to catch: a crossing nobody declared.
    b2 = AuthorisationBoundary("agency-llm-enclave")
    b2.declare("gateway", True, list(MODERATE_BASELINE))
    b2.declare("telemetry_sidecar", False, ["CA-3"])
    b2.connect("gateway", "telemetry_sidecar", declared=False)
    assert not b2.coverage()["ato_ready"], b2.coverage()
    assert len(b2.undeclared_crossings()) == 1, b2.undeclared_crossings()

    try:
        b2.declare("rogue", True, ["XX-1"])
        raise AssertionError("accepted a control outside the baseline")
    except KeyError:
        pass

    print("FedRAMP Moderate boundary + control map: OK")
    print(f"  controls covered {len(cov['covered'])}/{len(MODERATE_BASELINE)}, "
          f"undeclared crossings {len(b2.undeclared_crossings())}")

The blueprint

Frozen Golden Set Release Gate — the request path, with the decision points that must exist before the first model call:

flowchart LR
    CAND[Candidate model] --> SC[Score per slice]
    BASE[Baseline scores] --> CMP{Compare per slice}
    SC --> CMP
    CMP -->|slice regressed| BLOCK[Refuse release]
    CMP -->|below floor| BLOCK
    CMP -->|all slices hold| REL[Release + decision record]
    BLOCK --> REC[Record which slice and by how much]

Modular implementation framework

1. Frozen golden set — Versioned and immutable; editing it after freeze raises.
2. Slice taxonomy — Rare-but-critical cases are their own slice, not noise in the mean.
3. Per-slice thresholds — Tolerance for regression plus an absolute floor per slice.
4. Deterministic scorer — Same inputs, same score — a flaky scorer makes the gate decorative.
5. Release gate — Refuses on any slice regression even when the aggregate improves.
6. Decision record — What was compared, which slices moved, who accepted the risk.

Risk mitigation matrix

Each row carries an acceptance parameter rather than an adjective, because "low latency" cannot be signed off. Retention here is bound by per the data class retention schedule, enforced automatically.

Questions to answer before approval

• Which NIST 800-53 control does each component satisfy, and which components sit outside the authorisation boundary?
  Each component carries its control identifiers in configuration, and the boundary diagram is generated from that configuration so it cannot drift from reality.
• Which slices did the candidate model get worse on, and by how much?
  The gate reports a per-slice delta for every slice, and the decision record keeps it. An aggregate alone cannot answer this, which is why it is not the gate.
• What stops someone editing the evaluation set until the release passes?
  The set is frozen on publish — mutation raises rather than warns, and a change requires a new version with its own identifier recorded on every subsequent decision.
• If the aggregate score improves by two points, can the release still be refused?
  Yes, and it must be. A slice regression beyond tolerance, or any breach of a named slice's floor, refuses the release regardless of the aggregate.
• Where is the record of who accepted a known regression, and on what basis?
  In the decision record attached to the release: the slices that moved, the magnitude, and the named person who accepted it. An unaccepted regression cannot ship.
• How do you know the scorer gives the same answer twice?
  The scorer is deterministic and any sampling is seeded, with the seed recorded. The same candidate against the same frozen set produces the same report.

Related reference architectures

• Part of enterprise ai compliance architecture.
• Where this interacts with another regime, see fedRAMP Moderate reference architecture for enterprise LLM deployment.
• This pattern also governs fedRAMP Moderate prompt injection defence architecture for enterprise LLM.
• fedRAMP Moderate data governance framework for enterprise LLM deployment covers the adjacent case.
• A different failure mode of the same component: fedRAMP Moderate provider failover and degradation architecture.
• If this is the blocker in front of you: enterprise AI infrastructure review.