FedRAMP Moderate data governance framework for enterprise LLM deployment — Reference Architecture

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

The control that resolves this is a Classification-Routed Multi-Cloud Estate, 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.

The architecture friction point

Workloads span several providers and regions, and the decision of which model may see which data lives in prompt text, a wiki page, or a developer's judgement. That is not a control, so the governance board cannot approve the estate even though every individual component is defensible.

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.

Controls and acceptance parameters

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.

Technical parameters

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

Review questions and answers

• 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.
• What function decides which destination may see RESTRICTED data, and where does it run?
  The routing gate, evaluated before egress. It is a deterministic function of the data's classification and the destination's attested properties, and it fails closed when no destination is lawful.
• Show me the recorded reason for a denial last week.
  Every allow and every deny is written to the decision log with the reason — which destinations were rejected and on which property — so a denial is explainable months later.
• When a document is embedded, does the embedding carry its classification — and is that enforced on read?
  Yes. The classification tag propagates into every derived store, including embeddings, caches and eval sets, and is enforced on read as well as write.
• What happens when a provider adds a subprocessor you have not disclosed?
  The drift detector alerts when a destination's attested properties change, and routing is re-evaluated against the new attestation. Attestations are versioned data, not a one-off questionnaire.
• How do you prove processing stayed in the contracted region?
  The provider endpoint is pinned per region and the served region is asserted on the response; a mismatch fails the request rather than completing it quietly.

Reference architecture

Classification-Routed Multi-Cloud Estate — the request path, with the decision points that must exist before the first model call:

flowchart TB
    IN[Request payload] --> CLS[Deterministic classifier<br/>regex + Luhn/checksum]
    CLS --> LV{Classification}
    LV -->|PUBLIC / INTERNAL| D1[shared-us]
    LV -->|CONFIDENTIAL| D2[eu-pooled<br/>DPA required]
    LV -->|RESTRICTED| D3[eu-dedicated<br/>zero-retention + subprocessors]
    LV -->|no lawful destination| DN[Deny + reason]
    D1 --> LOG[(Decision log)]
    D2 --> LOG
    D3 --> LOG
    DN --> LOG
    D3 --> LIN[Lineage tag on<br/>embeddings / cache / evals]
    ATT[Destination attestations] -.-> LV
    ATT --> DR[Drift detector]

The parts you have to build

1. Deterministic classifier — Pattern plus validator (Luhn, checksum) so the gate does not cry wolf.
2. Destination attestations — Region, max class, retention, DPA, subprocessor disclosure — as data.
3. Routing gate — First lawful destination in preference order; cost can never override a control.
4. Decision log — A recorded reason for every allow AND every deny.
5. Lineage tags — Classification travels with derived artifacts: embeddings, caches, evals.
6. Drift detector — Alerts when a destination's attested properties change.

Reference implementation — Classification-driven routing gate with recorded reasons

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.

"""Classification-driven routing gate for a multi-cloud LLM estate.

Why this shape: "which model may see this data" is a governance decision, and
if it lives in prompt text or a developer's judgement it is not a control. It
has to be a deterministic function of the data's classification and the
destination's attested properties, evaluated before egress.

The gate below is the smallest thing that an actual data-governance board will
accept: classification in, allowed destinations out, with a recorded reason for
every allow and every deny.
"""
import re
from dataclasses import dataclass
from enum import IntEnum


class Class(IntEnum):
    PUBLIC = 0
    INTERNAL = 1
    CONFIDENTIAL = 2
    RESTRICTED = 3      # PII / PHI / material non-public


DETECTORS = {
    "email": re.compile(r"[\w.+-]+@[\w-]+\.[\w.]{2,}"),
    "iban": re.compile(r"\b[A-Z]{2}\d{2}[A-Z0-9]{10,30}\b"),
    "card": re.compile(r"\b(?:\d[ -]?){13,19}\b"),
    "ssn": re.compile(r"\b\d{3}-\d{2}-\d{4}\b"),
    "mrn": re.compile(r"\bMRN[:\s-]*\d{6,10}\b", re.I),
}


def luhn(number: str) -> bool:
    """Card detectors without Luhn produce false positives on order numbers,
    and a gate that cries wolf gets switched off."""
    digits = [int(c) for c in re.sub(r"\D", "", number)]
    if len(digits) < 13:
        return False
    checksum, parity = 0, len(digits) % 2
    for i, d in enumerate(digits):
        if i % 2 == parity:
            d *= 2
            if d > 9:
                d -= 9
        checksum += d
    return checksum % 10 == 0


def classify(text: str, floor: Class = Class.INTERNAL) -> tuple:
    """-> (classification, evidence). Deterministic and explainable."""
    found = []
    for name, rx in DETECTORS.items():
        for m in rx.finditer(text or ""):
            if name == "card" and not luhn(m.group(0)):
                continue
            found.append(name)
            break
    return (Class.RESTRICTED if found else floor), sorted(set(found))


@dataclass(frozen=True)
class Destination:
    name: str
    region: str
    max_class: Class
    zero_retention: bool
    dpa_signed: bool
    subprocessors_disclosed: bool


class Denied(Exception):
    pass


def route(text: str, destinations: list, required_region: str = None) -> dict:
    """Pick the first destination whose ATTESTED properties cover the data.

    Order matters: destinations are supplied in preference order (cheapest or
    fastest first) and the gate takes the first one that is lawful, so cost
    optimisation can never override a control.
    """
    level, evidence = classify(text)
    reasons = []
    for d in destinations:
        if d.max_class < level:
            reasons.append(f"{d.name}: max_class {d.max_class.name} < {level.name}")
            continue
        if required_region and d.region != required_region:
            reasons.append(f"{d.name}: region {d.region} != {required_region}")
            continue
        if level >= Class.RESTRICTED and not d.zero_retention:
            reasons.append(f"{d.name}: retains prompts, unusable for {level.name}")
            continue
        if level >= Class.CONFIDENTIAL and not d.dpa_signed:
            reasons.append(f"{d.name}: no DPA on file")
            continue
        if level >= Class.RESTRICTED and not d.subprocessors_disclosed:
            reasons.append(f"{d.name}: subprocessors undisclosed")
            continue
        return {"allowed": True, "destination": d.name,
                "classification": level.name, "evidence": evidence,
                "rejected": reasons}
    raise Denied(f"no lawful destination for {level.name} data: {reasons}")


if __name__ == "__main__":
    dests = [
        Destination("shared-us", "us-east-1", Class.INTERNAL, False, True, False),
        Destination("eu-pooled", "eu-west-1", Class.CONFIDENTIAL, False, True, True),
        Destination("eu-dedicated", "eu-west-1", Class.RESTRICTED, True, True, True),
    ]
    print(route("Summarise this quarter's churn drivers.", dests))
    print(route("Patient MRN 4471902 reports side effects.", dests,
                required_region="eu-west-1"))
    try:
        route("Card 4111111111111111 was declined.",
              [d for d in dests if not d.zero_retention])
    except Denied as e:
        print("correctly denied:", str(e)[:96])

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())}")

See also

• Part of enterprise ai data governance.
• A different failure mode of the same component: PCI DSS 4.0 data governance framework for enterprise LLM deployment.
• For the related constraint, see SOX data governance framework for enterprise LLM deployment.
• For the related constraint, see SOC 2 Type II data governance framework for enterprise LLM deployment.
• EU AI Act data governance framework for enterprise LLM deployment covers the adjacent case.
• If this is the blocker in front of you: enterprise AI infrastructure review.